Search results for: fruit fly optimization algorithm

Authors: M. H. Hussain, I. Musirin, A. F. Abidin, S. R. A. Rahim

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This paper presents the implementation of Artificial Bees Colony (ABC) algorithm in solving Directional OverCurrent Relays (DOCRs) coordination problem for near-end faults occurring in fixed network topology. The coordination optimization of DOCRs is formulated as linear programming (LP) problem. The objective function is introduced to minimize the operating time of the associated relay which depends on the time multiplier setting. The proposed technique is to taken as a technique for comparison purpose in order to highlight its superiority. The proposed algorithms have been tested successfully on 8 bus test system. The simulation results demonstrated that the ABC algorithm which has been proved to have good search ability is capable in dealing with constraint optimization problems.

Keywords: artificial bees colony, directional overcurrent relay coordination problem, relay settings, time multiplier setting

Procedia PDF Downloads 300

Authors: Sani M. Lawal, Idris Musa, Aliyu D. Usman

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The Pareto approach of optimal solutions in a search space that evolved in multi-objective optimization problems is adopted in this paper, which stands for a set of solutions in the search space. This paper aims at presenting an optimal placement of Distributed Generation (DG) in radial distribution networks with an optimal size for minimization of power loss and voltage deviation as well as maximizing voltage profile of the networks. And these problems are formulated using particle swarm optimization (PSO) as a constraint nonlinear optimization problem with both locations and sizes of DG being continuous. The objective functions adopted are the total active power loss function and voltage deviation function. The multiple nature of the problem, made it necessary to form a multi-objective function in search of the solution that consists of both the DG location and size. The proposed PSO algorithm is used to determine optimal placement and size of DG in a distribution network. The output indicates that PSO algorithm technique shows an edge over other types of search methods due to its effectiveness and computational efficiency. The proposed method is tested on the standard IEEE 34-bus and validated with 33-bus test systems distribution networks. Results indicate that the sizing and location of DG are system dependent and should be optimally selected before installing the distributed generators in the system and also an improvement in the voltage profile and power loss reduction have been achieved.

Keywords: distributed generation, pareto, particle swarm optimization, power loss, voltage deviation

Procedia PDF Downloads 328

Authors: Bezhan Ghvaberidze

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A multiple criteria optimization approach for the solution of the Fuzzy Vehicle Routing Problem (FVRP) is proposed. For the possibility environment the levels of movements between customers are calculated by the constructed simulation interactive algorithm. The first criterion of the bi-criteria optimization problem - minimization of the expectation of total fuzzy travel time on closed routes is constructed for the FVRP. A new, second criterion – maximization of feasibility of movement on the closed routes is constructed by the Choquet finite averaging operator. The FVRP is reduced to the bi-criteria partitioning problem for the so called “promising” routes which were selected from the all admissible closed routes. The convenient selection of the “promising” routes allows us to solve the reduced problem in the real-time computing. For the numerical solution of the bi-criteria partitioning problem the -constraint approach is used. An exact algorithm is implemented based on D. Knuth’s Dancing Links technique and the algorithm DLX. The Main objective was to present the new approach for FVRP, when there are some difficulties while moving on the roads. This approach is called FVRP for extreme conditions (FVRP-EC) on the roads. Also, the aim of this paper was to construct the solving model of the constructed FVRP. Results are illustrated on the numerical example where all Pareto-optimal solutions are found. Also, an approach for more complex model FVRP with time windows was developed. A numerical example is presented in which optimal routes are constructed for extreme conditions on the roads.

Keywords: combinatorial optimization, Fuzzy Vehicle routing problem, multiple objective programming, possibility theory

Procedia PDF Downloads 447

Authors: Muruvvet Ilgin, Ilknur Agca

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Common mulberry (Morus levigate Wall.) and purple mulberry (Morus rubra L.) species which were selected from different regions of Turkey were used as material in order to determine their performance. Therefore, phenological observations, pomological analysis (fruit size, fruit weight, fruit stalk length, acidity and TSS (Total Soluble Solids) and phytochemical properties organic acids (oxalic acid, succinic acid, citric acid, fumaric acid and malic acid) and vitamin C (ascorbic acid) total phenolics and antioxidant capacity values of mulberries) were determined. Phenological observations of seven different periods were also identified. Fruit weight values varied between 3.48 to 4.26 g. TSS contents value were from 14.36 to 21.30%, and fruit acidity was determined between 0.29 to 2.02%. The amount of ascorbic acid of Finger mulberry (Morus levigate Wall.) and purple mulberry (Morus rubra L.) species were identified as 35.60% and 363.28%. The highest value of total phenolic contents belonged to with a finger mulberry genotypes P1 934.80 mg/100g whereas the lowest one was of purple mulberry genotypes 278.70 mg/100g. FRAP and TEAC methods were used for determination of antioxidant capacity of the values of 0.58-22.65 micromol TE/kg and 20.34-31.6 micromol TE/kg. Total phenolics contents and antioxidant capacity strongly depends on fruit color intensity with a positive correlation. The obtained results have been found to be important as a source of future pharmacological studies and pomological and breeding programs.

Keywords: mulberry, phenology, phytochemical property, pomology

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Authors: Nicolae Bold, Daniel Nijloveanu

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The idea of cropping-system is a method used by farmers. It is an environmentally-friendly method, protecting the natural resources (soil, water, air, nutritive substances) and increase the production at the same time, taking into account some crop particularities. The combination of this powerful method with the concepts of genetic algorithms results into a possibility of generating sequences of crops in order to form a rotation. The usage of this type of algorithms has been efficient in solving problems related to optimization and their polynomial complexity allows them to be used at solving more difficult and various problems. In our case, the optimization consists in finding the most profitable rotation of cultures. One of the expected results is to optimize the usage of the resources, in order to minimize the costs and maximize the profit. In order to achieve these goals, a genetic algorithm was designed. This algorithm ensures the finding of several optimized solutions of cropping-systems possibilities which have the highest profit and, thus, which minimize the costs. The algorithm uses genetic-based methods (mutation, crossover) and structures (genes, chromosomes). A cropping-system possibility will be considered a chromosome and a crop within the rotation is a gene within a chromosome. Results about the efficiency of this method will be presented in a special section. The implementation of this method would bring benefits into the activity of the farmers by giving them hints and helping them to use the resources efficiently.

Keywords: chromosomes, cropping, genetic algorithm, genes

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

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Authors: P. W. Tsai, W. L. Hong, C. W. Chen, C. Y. Chen

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In this paper, we present a neural network (NN) based approach represent a nonlinear Tagagi-Sugeno (T-S) system. A linear differential inclusion (LDI) state-space representation is utilized to deal with the NN models. Taking advantage of the LDI representation, the stability conditions and controller design are derived for a class of nonlinear structural systems. Moreover, the concept of utilizing the Parallel Particle Swarm Optimization (PPSO) algorithm to solve the common P matrix under the stability criteria is given in this paper.

Keywords: Lyapunov stability, parallel particle swarm optimization, linear differential inclusion, artificial intelligence

Procedia PDF Downloads 623

Authors: H. B. Torane, M. C. Kasture, S. S. Prabhudesai, P. B. Sanap, V. N. Palsande, J. J. Palkar

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The present investigation entitled “Response of briquettes application with different coating materials on yield and quality of Cucumber [Cucumis sativus (L.)]” was conducted at Central Experiment Center, Wakawali during kharif season 2013. The field experiment was laid out in Factorial Randomized Block Design with three replicate. The four coating materials viz., Co – Non coating, C1 – Wax coating, C2 – Jaggary coating, and C3 – Tar coating was applied to Konkan Annapurna Briquette along with three sub treatments of application time i.e B1 – ½ at sowing, B2 - ½ at sowing and ½ at 30 days after sowing and B3 - 1/3 at sowing, 1/3 at 30 days after sowing and 1/3 at 60 days after sowing. It was observed that the application of tar coated Konkan Annapurna Briquettes (KAB) in three times @1/3 quantity of briquettes at sowing time, 1/3 quantity of briquettes at 30 days after sowing and 1/3 quantity of briquettes at 60 days after sowing was found promising to enhancing the cucumber fruit yield, higher vine length, number of fruits vine-1, girth of fruit, length of fruit. It was also observed that the quality of the cucumber fruit increased in terms of ascorbic acid. UB-Godavari forms of briquettes .i.e. are promising source of N, P2O5 and K2O fertilizers as compared to straight fertilizers for enhancing green cucumber fruit yield of Sheetal variety of cucumber in lateritic soil. Amongst the three types of coated briquettes, the tar coated briquettes application was found to be superior for increasing cucumber fruit yield applied in three times @1/3 quantity of briquettes at sowing time, 1/3 quantity of briquettes at 30 days after sowing and 1/3 quantity of briquettes at 60 days after sowing @ 5 briquettes per plant at an interval of 30 days after sowing.

Keywords: briquettes, coating, yield, tar, wax and quality

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Authors: KumarShashvat, ArshpreetKaur, RajeshKumar, Raman Chadha

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Wireless sensor networks have principal characteristic of having restricted energy and with limitation that energy of the nodes cannot be replenished. To increase the lifetime in this scenario WSN route for data transmission is opted such that utilization of energy along the selected route is negligible. For this energy efficient network, dandy infrastructure is needed because it impinges the network lifespan. Clustering is a technique in which nodes are grouped into disjoints and non–overlapping sets. In this technique data is collected at the cluster head. In this paper, Adaptive-PSO algorithm is proposed which forms energy aware clusters by minimizing the cost of locating the cluster head. The main concern is of the suitability of the swarms by adjusting the learning parameters of PSO. Particle Swarm Optimization converges quickly at the beginning stage of the search but during the course of time, it becomes stable and may be trapped in local optima. In suggested network model swarms are given the intelligence of the spiders which makes them capable enough to avoid earlier convergence and also help them to escape from the local optima. Comparison analysis with traditional PSO shows that new algorithm considerably enhances the performance where multi-dimensional functions are taken into consideration.

Keywords: Particle Swarm Optimization, adaptive – PSO, comparison between PSO and A-PSO, energy efficient clustering

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Authors: E. Younsi, H. Andriamboavonjy, A. David, S. Dokou, B. Lemrabet

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Software and time optimization are very important factors in financial markets, which are competitive fields, and emergence of new computer tools further stresses the challenge. In this context, any improvement of technical indicators which generate a buy or sell signal is a major issue. Thus, many tools have been created to make them more effective. This worry about efficiency has been leading in present paper to seek best (and most innovative) way giving largest improvement in these indicators. The approach consists in attaching a signature to frequent market configurations by application of frequent patterns extraction method which is here most appropriate to optimize investment strategies. The goal of proposed trading algorithm is to find most accurate signatures using back testing procedure applied to technical indicators for improving their performance. The problem is then to determine the signatures which, combined with an indicator, outperform this indicator alone. To do this, the FP-Tree algorithm has been preferred, as it appears to be the most efficient algorithm to perform this task.

Keywords: quantitative analysis, back-testing, computational models, apriori algorithm, pattern recognition, data mining, FP-tree

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Authors: Phawin Sangsuvan, Chutimet Srinilta

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This Paper presents the applications of Particle Swarm Optimization (PSO) Method for Thai optical character recognition (OCR). OCR consists of the pre-processing, character recognition and post-processing. Before enter into recognition process. The Character must be “Prepped” by pre-processing process. The PSO is an optimization method that belongs to the swarm intelligence family based on the imitation of social behavior patterns of animals. Route of each particle is determined by an individual data among neighborhood particles. The interaction of the particles with neighbors is the advantage of Particle Swarm to determine the best solution. So PSO is interested by a lot of researchers in many difficult problems including character recognition. As the previous this research used a Projection Histogram to extract printed digits features and defined the simple Fitness Function for PSO. The results reveal that PSO gives 67.73% for testing dataset. So in the future there can be explored enhancement the better performance of PSO with improve the Fitness Function.

Keywords: character recognition, histogram projection, particle swarm optimization, pattern recognition techniques

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Authors: Abdullah Eqal Al Mazrooei

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In this paper, we introduce a mathematical algorithm which is used for estimating the states in the bilinear systems. This algorithm uses a special linearization of the second-order term by using the best available information about the state of the system. This technique makes our algorithm generalizes the well-known Kalman estimators. The system which is used here is of the bilinear class, the evolution of this model is linear-bilinear in the state of the system. Our algorithm can be used with linear and bilinear systems. We also here introduced a real application for the new algorithm to prove the feasibility and the efficiency for it.

Keywords: estimation algorithm, bilinear systems, Kakman filter, second order linearization

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Authors: Srushti Bhatt, M. B. Kiran

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Two-sided assembly line balancing problem is yet to be addressed simply to compete for the global market for manufacturers. The task assigned in an ordered sequence to get optimum performance of the system is known as assembly line balancing problem mainly classified as single and two sided. It is very challenging in manufacturing industries to balance two-sided assembly line, wherein the set of sequential workstations the task operations are performed in two sides of the line. The conflicting major objective in two-sided assembly line balancing problem is either to maximize /minimize the performance parameters. The present study emphases on combining different evolutionary algorithm; ant colony, Tabu search and petri net method; and compares their results of an algorithm for solving two-sided assembly line balancing problem. The concept of multi objective optimization of performance parameters is now a day adopted to make a decision involving more than one objective function to be simultaneously optimized. The optimum result can be expected among the selected methods using multi-objective optimization. The performance parameters considered in the present study are a number of workstation, slickness and smoothness index. The simulation of the assembly line balancing problem provides optimal results of classical and practical problems.

Keywords: Ant colony, petri net, tabu search, two sided ALBP

Procedia PDF Downloads 249

Authors: Narjes Dashti, Nida Ali, Magdy Montasser, Vineetha Cherian

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The influence of two PGPR strains, Pseudomonas aeruginosa and Stenotrophomonas rhizophilia, on fruit yields, pomological traits and chemical contents of tomato (Solanum lycopersicum) fruits were studied. The study was conducted separately on two different cultivar varieties of tomato, namely Supermarmande and UC82B. The results indicated that the presence of the PGPR almost doubled the average yield per plant. There was a significant improvement in the pomological qualities of the PGPR treated tomato fruits compared to the corresponding healthy treatments especially in traits such as the average fruit weight, height, and fruit volume. The chemical analysis of tomato fruits revealed that the presence of the PGPRs increased the total protein, lycopene, alkalinity and phenol content of the tomato fruits compared to the healthy controls. They had no influence on the reduced sugar, total soluble solids or the titerable acid content of fruits. However their presence reduced the amount of ascorbic acid in tomato fruits compared to the healthy controls.

Keywords: PGPR, tomato, fruit quality

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Authors: Bao Li, Aike Qiao, Gaoyang Li, Youjun Liu

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Background: Lumped parameter model (LPM) is a common numerical model for hemodynamic calculation. LPM uses circuit elements to simulate the human blood circulatory system. Physiological indicators and characteristics can be acquired through the model. However, due to the different physiological indicators of each individual, parameters in LPM should be personalized in order for convincing calculated results, which can reflect the individual physiological information. This study aimed to develop an automatic and effective optimization method to personalize the parameters in LPM of the blood circulatory system, which is of great significance to the numerical simulation of individual hemodynamics. Methods: A closed-loop LPM of the human blood circulatory system that is applicable for most persons were established based on the anatomical structures and physiological parameters. The patient-specific physiological data of 5 volunteers were non-invasively collected as personalized objectives of individual LPM. In this study, the blood pressure and flow rate of heart, brain, and limbs were the main concerns. The collected systolic blood pressure, diastolic blood pressure, cardiac output, and heart rate were set as objective data, and the waveforms of carotid artery flow and ankle pressure were set as objective waveforms. Aiming at the collected data and waveforms, sensitivity analysis of each parameter in LPM was conducted to determine the sensitive parameters that have an obvious influence on the objectives. Simulated annealing was adopted to iteratively optimize the sensitive parameters, and the objective function during optimization was the root mean square error between the collected waveforms and data and simulated waveforms and data. Each parameter in LPM was optimized 500 times. Results: In this study, the sensitive parameters in LPM were optimized according to the collected data of 5 individuals. Results show a slight error between collected and simulated data. The average relative root mean square error of all optimization objectives of 5 samples were 2.21%, 3.59%, 4.75%, 4.24%, and 3.56%, respectively. Conclusions: Slight error demonstrated good effects of optimization. The individual modeling algorithm developed in this study can effectively achieve the individualization of LPM for the blood circulatory system. LPM with individual parameters can output the individual physiological indicators after optimization, which are applicable for the numerical simulation of patient-specific hemodynamics.

Keywords: blood circulatory system, individual physiological indicators, lumped parameter model, optimization algorithm

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Authors: Tushar Bhardwaj

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Internet of Things (IoT) possesses a dynamic network where the network nodes (mobile devices) are added and removed constantly and randomly, hence the traffic distribution in the network is quite variable and irregular. The basic but very important part in any network is route searching. We have many conventional route searching algorithms like link-state, and distance vector algorithms but they are restricted to the static point to point network topology. In this paper we propose a model that uses the Ant Colony Algorithm for route searching. It is dynamic in nature and has positive feedback mechanism that conforms to the route searching. We have also embedded the concept of Non-Deterministic Finite Automata [NDFA] minimization to reduce the network to increase the performance. Results show that Ant Colony Algorithm gives the shortest path from the source to destination node and NDFA minimization reduces the broadcasting storm effectively.

Keywords: routing, ant colony algorithm, NDFA, IoT

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Lokesh Bhatt, Jayesh Rathod

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Hypertension is one of the most prevalent cardiovascular disorder. It is responsible for several other cardiovascular disorders. Although many drugs are available for the treatment of hypertension, still a large population has uncontrolled blood pressure. Thus there is an unmet need for new therapeutic approaches for the same. Fruit juice of Citrus paradise contains several flavonoids with vasodilatory activity. We hypothesized that alcoholic extract of Citrus paradise, which contains flavonoids, might attenuate hypertension. The objective of the present study was to evaluate the antihypertensive activity of alcoholic extract of Citrus paradise fruit juice in rats. Hypertension was induced using one clip one kidney model in rats. The renal artery was occluded for 4 h after removal of one kidney. Once stabilized, the ganglionic blockade was performed followed by removal of the arterial clip from the kidney. Removal of clip resulted in an increase in blood pressure which is due to release of renin from the kidney. Alcoholic extract of Citrus paradise fruit juice was then administered at 50 mg/kg and 100 mg/kg dose by intravenous injection. Blood pressure was monitored continuously. Alcoholic extract of Citrus paradise fruit juice reduced hypertension in dose-dependent manner. Antihypertensive activity was found to be associated with vasodilation. The results of the present study showed antihypertensive potential of alcoholic extract of Citrus paradise fruit juice.

Keywords: citrus paradise, alcoholic extract, one clip one kidney model, vasodilation

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Authors: Xiongxiong You, Zhanwen Niu

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Choosing an appropriate surrogate model plays an important role in surrogates-assisted evolutionary algorithms (SAEAs) since there are many types and different kernel functions in the surrogate model. In this paper, an adaptive selection of the best suitable surrogate model method is proposed to solve different kinds of expensive optimization problems. Firstly, according to the prediction residual error sum of square (PRESS) and different model selection strategies, the excellent individual surrogate models are integrated into multiple ensemble models in each generation. Then, based on the minimum root of mean square error (RMSE), the best suitable surrogate model is selected dynamically. Secondly, two methods with dynamic number of models and selection strategies are designed, which are used to show the influence of the number of individual models and selection strategy. Finally, some compared studies are made to deal with several commonly used benchmark problems, as well as a rotor system optimization problem. The results demonstrate the accuracy and robustness of the proposed method.

Keywords: adaptive selection, expensive optimization, rotor system, surrogates assisted evolutionary algorithms

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Authors: Kamel Cheriet

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In Algeria, Opuntia ficus Indica production is important. This seasonal fruit is a characteristic of arid and semi-arid regions. Taking into account its high content in antioxidants, it has an excellent nutritional value. The aim of this research is the prickly pear morphological and physicochemical characterization study which is widely present in the Arris (Batna, Algeria) area. The results of this experimental study are comparative to those of the same species from other world regions. The whole fruit weight is estimated to reach 63.38 g with a juice ratio of 71.42%, a pH of 5.54, moisture of 89.3% and a brix of 10.4°. The quantitative amount of the phenolic compounds of the fruit revealed contents of 20.65-45.70 mg / 100 g of MF for total polyphenols and 0.519 -0.591 mg / 100 g of MF for the flavonoids.

Keywords: functional composition, nutritionals properties, opuntia ficus indica, phenolic compounds

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Authors: Bingxi Huang, Yiqing Li, Marek Morzynski, Bernd R. Noack

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We propose a Kriging-based global optimization method for active flow control with multiple actuation parameters. This method is designed to converge quickly and avoid getting trapped into local minima. We follow the model-free explorative gradient method (EGM) to alternate between explorative and exploitive steps. This facilitates a convergence similar to a gradient-based method and the parallel exploration of potentially better minima. In contrast to EGM, both kinds of steps are performed with Kriging surrogate model from the available data. The explorative step maximizes the expected improvement, i.e., favors regions of large uncertainty. The exploitive step identifies the best location of the cost function from the Kriging surrogate model for a subsequent weight-biased linear-gradient descent search method. To verify the effectiveness and robustness of the improved Kriging-based optimization method, we have examined several comparative test problems of varying dimensions with limited evaluation budgets. The results show that the proposed algorithm significantly outperforms some model-free optimization algorithms like genetic algorithm and differential evolution algorithm with a quicker convergence for a given budget. We have also performed direct numerical simulations of the fluidic pinball (N. Deng et al. 2020 J. Fluid Mech.) on three circular cylinders in equilateral-triangular arrangement immersed in an incoming flow at Re=100. The optimal cylinder rotations lead to 44.0% net drag power saving with 85.8% drag reduction and 41.8% actuation power. The optimal results for active flow control based on this configuration have achieved boat-tailing mechanism by employing Coanda forcing and wake stabilization by delaying separation and minimizing the wake region.

Keywords: direct numerical simulations, flow control, kriging, stochastic optimization, wake stabilization

Procedia PDF Downloads 79

Authors: Gbenonsi A. Fabrice, Mama Sambo Sahadatou, Layode B. F. Rodolphe, Totin A. Felicien, Onzo Alexis, Karlsson M. Frida

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Interspecific competition occurs among arthropod pest species that share hosts, thereby influencing their population dynamics. In sub-Saharan Africa, the native fruit fly species Dacus vertebratus (Bezzi) and Dacus ciliatus (Loew) (Diptera: Tephritidae) and the exotic Zeugodacus cucurbitae (Coquillett) are concurrent pests in the same host fruits; hence competition among them is likely to occur. We explored interspecific competition among these three fruit fly species on zucchini (Cucurbita pepo L.) and watermelon (Citrullus lanatus Thunb) (Cucurbitaceae) to improve our understanding of the interaction between the species and their capacity to coexist. We exposed the vegetable fruits to different densities of fruit fly species and studied their behavioural activities, evaluating the extrinsic competition. To assess intrinsic competition and understand the effect of co-occurrence inside the fruits, eggs of the three fruit flies were pairwise inoculated into the same fruits. Results showed that the behaviour on the fruits differed between the species and that the interspecific competition affected their developmental time and larval survival in both watermelon and zucchini. Z. cucurbitae were more aggressive than the other species and managed to oviposit more frequently. Emergence was reduced for D. ciliatus and D. vertebratus when inoculated together with Z. cucurbitae in watermelon but not in zucchini. Physical confrontations were more common in zucchini than in watermelon and were more frequently won by Z. cucurbitae than D. vertebratus and D. ciliatus. Interspecific competition information obtained about behavioural differences and interaction effects, providing background for explaining the present fruit fly guild on certain Cucurbitaceae fruits in West Africa.

Keywords: behavioural activities, extrinsic competition, intrinsic competition, Tephritidae

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Authors: Duc Dong Do, Ngoc Ha Tran, Thanh Hai Dang, Cao Cuong Dang, Xuan Huan Hoang

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Global aligning two protein-protein interaction networks is an essentially important task in bioinformatics/computational biology field of study. It is a challenging and widely studied research topic in recent years. Accurately aligned networks allow us to identify functional modules of proteins and/ororthologous proteins from which unknown functions of a protein can be inferred. We here introduce a novel efficient heuristic global network alignment algorithm called FASTAn, including two phases: the first to construct an initial alignment and the second to improve such alignment by exerting a local optimization repeated procedure. The experimental results demonstrated that FASTAn outperformed the state-of-the-art global network alignment algorithm namely SPINAL in terms of both commonly used objective scores and the run-time.

Keywords: FASTAn, Heuristic algorithm, biological network alignment, protein-protein interaction networks

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Authors: Francis O. Nwawuru

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The convergence analysis of split monotone inclusion problems and fixed point problems of certain nonlinear mappings are investigated in the setting of real Hilbert spaces. Inertial extrapolation term in the spirit of Polyak is incorporated to speed up the rate of convergence. Under standard assumptions, a strong convergence of the proposed algorithm is established without computing the resolvent operator or involving Yosida approximation method. The stepsize involved in the algorithm does not depend on the spectral radius of the linear operator. Furthermore, applications of the proposed algorithm in solving some related optimization problems are also considered. Our result complements and extends numerous results in the literature.

Keywords: fixedpoint, hilbertspace, monotonemapping, resolventoperators

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Authors: Bhawat Chiamjinnawat

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The agri-food market transformation has implied market growth for the fruit industry in Thailand. This article focuses on analysis of farmer competence and farm resources which affect market strategies used by fruit farmers in Chanthaburi province of Thailand. The survey data were collected through the use of face-to-face interviews with structured questionnaires. This study identified 14 drivers related to farmer competence and farm resources of which some had significant effect on the decision to use either high-value markets or traditional markets. The results suggest that farmers who used high-value markets were better educated and they had longer experience and larger sized business. Identifying the important factors that match with the market transformation provides policy with opportunities to support the fruit farmers to increase their market power. Policies that promote business expansion of agricultural cooperatives and knowledge sharing among farmers are recommended to reduce limitations due to limited knowledge, low experience, and small business sizes.

Keywords: farmer competence, farm resources, fruit industry, high-value markets, Thailand

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Authors: Badr M. Alshammari, T. Guesmi

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This study presents a modified version of the artificial bee colony (ABC) algorithm by including a local search technique for solving the non-convex economic power dispatch problem. The local search step is incorporated at the end of each iteration. Total system losses, valve-point loading effects and prohibited operating zones have been incorporated in the problem formulation. Thus, the problem becomes highly nonlinear and with discontinuous objective function. The proposed technique is validated using an IEEE benchmark system with ten thermal units. Simulation results demonstrate that the proposed optimization algorithm has better convergence characteristics in comparison with the original ABC algorithm.

Keywords: economic power dispatch, artificial bee colony, valve-point loading effects, prohibited operating zones

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Authors: V. Pizarro-Carmona, S. Castano-Solis, M. Cortés-Carmona, J. Fraile-Ardanuy, D. Jimenez-Bermejo

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The purpose of this article is to optimize the Equivalent Electric Circuit Model (EECM) of different orders to obtain greater precision in the modeling of Li-ion battery packs. Optimization includes considering circuits based on 1RC, 2RC and 3RC networks, with a dependent voltage source and a series resistor. The parameters are obtained experimentally using tests in the time domain and in the frequency domain. Due to the high non-linearity of the behavior of the battery pack, Genetic Algorithm (GA) was used to solve and optimize the parameters of each EECM considered (1RC, 2RC and 3RC). The objective of the estimation is to minimize the mean square error between the measured impedance in the real battery pack and those generated by the simulation of different proposed circuit models. The results have been verified by comparing the Nyquist graphs of the estimation of the complex impedance of the pack. As a result of the optimization, the 2RC and 3RC circuit alternatives are considered as viable to represent the battery behavior. These battery pack models are experimentally validated using a hardware-in-the-loop (HIL) simulation platform that reproduces the well-known New York City cycle (NYCC) and Federal Test Procedure (FTP) driving cycles for electric vehicles. The results show that using GA optimization allows obtaining EECs with 2RC or 3RC networks, with high precision to represent the dynamic behavior of a battery pack in vehicular applications.

Keywords: Li-ion battery packs modeling optimized, EECM, GA, electric vehicle applications

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Authors: Saad Bougrine, Salma Ouchraa, Belaid Ahiod, Abdelhakim Ameur El Imrani

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Ant colony optimization is an ant algorithm framework that took inspiration from foraging behaviour of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This process allows fine and local exploration of search space and permits optimal solution to be improved.

Keywords: acoustic communication, ant colony optimization, local search, traveling salesman problem

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Authors: Christian Angerer, Markus Lienkamp

Abstract:

Electric vehicles offer a high variety of possible drivetrain topologies with up to 4 motors. Multi-motor-designs can have several advantages regarding traction, vehicle dynamics, safety and even efficiency. With a rising number of motors, the whole drivetrain becomes more complex. All permutations of gearings, drivetrain-layouts, motor-types and –sizes lead up in a very large solution space. Single elements of this solution space can be analyzed by simulation methods. In addition to longitudinal vehicle behavior, which most optimization-approaches are restricted to, also lateral dynamics are important for vehicle dynamics, stability and efficiency. In order to compete large solution spaces and to find an optimal result, genetic algorithm based optimization is state-of-the-art. As lateral dynamics simulation is way more CPU-intensive, optimization takes much more time than in case of longitudinal-only simulation. Therefore, this paper shows an approach how to create meta-models from a 14-degree of freedom vehicle model in order to enable a numerically efficient drivetrain-layout optimization process under consideration of lateral dynamics. Different meta-modelling approaches such as neural networks or DoE are implemented and comparatively discussed.

Keywords: driving dynamics, drivetrain layout, genetic optimization, meta-modeling, lateral dynamicx

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Authors: Mehdi Shahbazian, Alireza Aarabi, Mohsen Hadiyan

Abstract:

Proportional-Integral-Derivative (PID) controllers are the most widely used controllers in industry because of its simplicity and robustness. Different values of PID parameters make different step response, so an increasing amount of literature is devoted to proper tuning of PID controllers. The problem merits further investigation as traditional tuning methods make large control signal that can damages the system but using evolutionary algorithms based tuning methods improve the control signal and closed loop performance. In this paper three tuning methods for PID controllers have been studied namely Ziegler and Nichols, which is traditional tuning method and evolutionary algorithms based tuning methods, that are, Genetic algorithm and particle swarm optimization. To examine the validity of PSO and GA tuning methods a comparative analysis of DC motor plant is studied. Simulation results reveal that evolutionary algorithms based tuning method have improved control signal amplitude and quality factors of the closed loop system such as rise time, integral absolute error (IAE) and maximum overshoot.

Keywords: evolutionary algorithm, genetic algorithm, particle swarm optimization, PID controller

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