Search results for: heuristic method

Authors: Ellips Masehian, Davoud Sedighizadeh

Abstract:

This paper reviews the major contributions to the Motion Planning (MP) field throughout a 35-year period, from classic approaches to heuristic algorithms. Due to the NP-Hardness of the MP problem, heuristic methods have outperformed the classic approaches and have gained wide popularity. After surveying around 1400 papers in the field, the amount of existing works for each method is identified and classified. Especially, the history and applications of numerous heuristic methods in MP is investigated. The paper concludes with comparative tables and graphs demonstrating the frequency of each MP method's application, and so can be used as a guideline for MP researchers.

Keywords: Robot motion planning, Heuristic algorithms.

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Authors: M. Fathi, M. J. Alvarez, V. Rodríguez

Abstract:

Assembly line balancing is a very important issue in mass production systems due to production cost. Although many studies have been done on this topic, but because assembly line balancing problems are so complex they are categorized as NP-hard problems and researchers strongly recommend using heuristic methods. This paper presents a new heuristic approach called the critical task method (CTM) for solving U-shape assembly line balancing problems. The performance of the proposed heuristic method is tested by solving a number of test problems and comparing them with 12 other heuristics available in the literature to confirm the superior performance of the proposed heuristic. Furthermore, to prove the efficiency of the proposed CTM, the objectives are increased to minimize the number of workstation (or equivalently maximize line efficiency), and minimizing the smoothness index. Finally, it is proven that the proposed heuristic is more efficient than the others to solve the U-shape assembly line balancing problem.

Keywords: Critical task method, Heuristic, Line balancingproblem, U-shape

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Authors: S. Khanpour, A. Movaghar

Abstract:

The one of best robust search technique on large scale search area is heuristic and meta heuristic approaches. Especially in issue that the exploitation of combinatorial status in the large scale search area prevents the solution of the problem via classical calculating methods, so such problems is NP-complete. in this research, the problem of winner determination in combinatorial auctions have been formulated and by assessing older heuristic functions, we solve the problem by using of genetic algorithm and would show that this new method would result in better performance in comparison to other heuristic function such as simulated annealing greedy approach.

Keywords: Bids, genetic algorithm, heuristic, metaheuristic, simulated annealing greedy.

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Authors: Stephen C. H. Leung, Defu Zhang

Abstract:

This paper addresses a stock-cutting problem with rotation of items and without the guillotine cutting constraint. In order to solve the large-scale problem effectively and efficiently, we propose a simple but fast heuristic algorithm. It is shown that this heuristic outperforms the latest published algorithms for large-scale problem instances.

Keywords: Combinatorial optimization, heuristic, large-scale, stock-cutting.

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Authors: Maha Idrissi Aouad, Rene Schott, Olivier Zendra

Abstract:

Reducing energy consumption of embedded systems requires careful memory management. It has been shown that Scratch- Pad Memories (SPMs) are low size, low cost, efficient (i.e. energy saving) data structures directly managed at the software level. In this paper, the focus is on heuristic methods for SPMs management. A method is efficient if the number of accesses to SPM is as large as possible and if all available space (i.e. bits) is used. A Tabu Search (TS) approach for memory management is proposed which is, to the best of our knowledge, a new original alternative to the best known existing heuristic (BEH). In fact, experimentations performed on benchmarks show that the Tabu Search method is as efficient as BEH (in terms of energy consumption) but BEH requires a sorting which can be computationally expensive for a large amount of data. TS is easy to implement and since no sorting is necessary, unlike BEH, the corresponding sorting time is saved. In addition to that, in a dynamic perspective where the maximum capacity of the SPM is not known in advance, the TS heuristic will perform better than BEH.

Keywords: Energy consumption, memory allocation management, optimization, tabu search heuristic.

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Authors: Supriya Arya, Sunita Dhingra

Abstract:

Multiprocessor task scheduling problem for dependent and independent tasks is computationally complex problem. Many methods are proposed to achieve optimal running time. As the multiprocessor task scheduling is NP hard in nature, therefore, many heuristics are proposed which have improved the makespan of the problem. But due to problem specific nature, the heuristic method which provide best results for one problem, might not provide good results for another problem. So, Simulated Annealing which is meta heuristic approach is considered. It can be applied on all types of problems. However, due to many runs, meta heuristic approach takes large computation time. Hence, the hybrid approach is proposed by combining the Duplication Scheduling Heuristic and Simulated Annealing (SA) and the makespan results of Simple Simulated Annealing and Hybrid approach are analyzed.

Keywords: Multiprocessor task scheduling Problem, Makespan, Duplication Scheduling Heuristic, Simulated Annealing, Hybrid Approach.

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Authors: S. Raja Balachandar, K. Kannan

Abstract:

A new Meta heuristic approach called "Randomized gravitational emulation search algorithm (RGES)" for solving large size set covering problems has been designed. This algorithm is found upon introducing randomization concept along with the two of the four primary parameters -velocity- and -gravity- in physics. A new heuristic operator is introduced in the domain of RGES to maintain feasibility specifically for the set covering problem to yield best solutions. The performance of this algorithm has been evaluated on a large set of benchmark problems from OR-library. Computational results showed that the randomized gravitational emulation search algorithm - based heuristic is capable of producing high quality solutions. The performance of this heuristic when compared with other existing heuristic algorithms is found to be excellent in terms of solution quality.

Keywords: Set covering problem, velocity, gravitational force, Newton's law, meta heuristic, combinatorial optimization.

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Authors: S. Raja Balachandar, K.Kannan

Abstract:

A new Meta heuristic approach called "Randomized gravitational emulation search algorithm (RGES)" for solving vertex covering problems has been designed. This algorithm is found upon introducing randomization concept along with the two of the four primary parameters -velocity- and -gravity- in physics. A new heuristic operator is introduced in the domain of RGES to maintain feasibility specifically for the vertex covering problem to yield best solutions. The performance of this algorithm has been evaluated on a large set of benchmark problems from OR-library. Computational results showed that the randomized gravitational emulation search algorithm - based heuristic is capable of producing high quality solutions. The performance of this heuristic when compared with other existing heuristic algorithms is found to be excellent in terms of solution quality.

Keywords: Vertex covering Problem, Velocity, Gravitational Force, Newton's Law, Meta Heuristic, Combinatorial optimization.

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Authors: S. Balaji, N. Revathi

Abstract:

Connected dominating set (CDS) problem in unit disk graph has signi£cant impact on an ef£cient design of routing protocols in wireless sensor networks, where the searching space for a route is reduced to nodes in the set. A set is dominating if all the nodes in the system are either in the set or neighbors of nodes in the set. In this paper, a simple and ef£cient heuristic method is proposed for £nding a minimum connected dominating set (MCDS) in ad hoc wireless networks based on the new parameter support of vertices. With this parameter the proposed heuristic approach effectively £nds the MCDS of a graph. Extensive computational experiments show that the proposed approach outperforms the recently proposed heuristics found in the literature for the MCD

Keywords: ad hoc wireless networks, dominating sets, unit disk graphs, heuristic.

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Authors: S. Raja Balachandar, K.Kannan

Abstract:

This paper presents a heuristic approach to solve the Generalized Assignment Problem (GAP) which is NP-hard. It is worth mentioning that many researches used to develop algorithms for identifying the redundant constraints and variables in linear programming model. Some of the algorithms are presented using intercept matrix of the constraints to identify redundant constraints and variables prior to the start of the solution process. Here a new heuristic approach based on the dominance property of the intercept matrix to find optimal or near optimal solution of the GAP is proposed. In this heuristic, redundant variables of the GAP are identified by applying the dominance property of the intercept matrix repeatedly. This heuristic approach is tested for 90 benchmark problems of sizes upto 4000, taken from OR-library and the results are compared with optimum solutions. Computational complexity is proved to be O(mn2) of solving GAP using this approach. The performance of our heuristic is compared with the best state-ofthe- art heuristic algorithms with respect to both the quality of the solutions. The encouraging results especially for relatively large size test problems indicate that this heuristic approach can successfully be used for finding good solutions for highly constrained NP-hard problems.

Keywords: Combinatorial Optimization Problem, Generalized Assignment Problem, Intercept Matrix, Heuristic, Computational Complexity, NP-Hard Problems.

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Authors: Jindřiška Šedová, Miloš Šeda

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This paper summarizes and compares approaches to solving the knapsack problem and its known application in capital budgeting. The first approach uses deterministic methods and can be applied to small-size tasks with a single constraint. We can also apply commercial software systems such as the GAMS modelling system. However, because of NP-completeness of the problem, more complex problem instances must be solved by means of heuristic techniques to achieve an approximation of the exact solution in a reasonable amount of time. We show the problem representation and parameter settings for a genetic algorithm framework.

Keywords: Capital budgeting, knapsack problem, GAMS, heuristic method, genetic algorithm.

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Authors: Guang Zeng, Jin Huang, Zicai Li

Abstract:

In this paper, we research the standard 13-point difference schemes for solving the biharmonic equation. Heuristic method is applied to judging the stability of multi-level difference schemes of the biharmonic equation. It is showed that the standard 13-point difference schemes are stable.

Keywords: Finite-difference equation, computational stability, hirt method.

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Authors: Miloš Šeda

Abstract:

Resource-constrained project scheduling is an NPhard optimisation problem. There are many different heuristic strategies how to shift activities in time when resource requirements exceed their available amounts. These strategies are frequently based on priorities of activities. In this paper, we assume that a suitable heuristic has been chosen to decide which activities should be performed immediately and which should be postponed and investigate the resource-constrained project scheduling problem (RCPSP) from the implementation point of view. We propose an efficient routine that, instead of shifting the activities, extends their duration. It makes it possible to break down their duration into active and sleeping subintervals. Then we can apply the classical Critical Path Method that needs only polynomial running time. This algorithm can simply be adapted for multiproject scheduling with limited resources.

Keywords: Project management, resource-constrained scheduling, NP-hard problem, CPM, heuristic method.

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Authors: K. Krishna Veni, S. Raja Balachandar

Abstract:

This paper presents a heuristic to solve large size 0-1 Multi constrained Knapsack problem (01MKP) which is NP-hard. Many researchers are used heuristic operator to identify the redundant constraints of Linear Programming Problem before applying the regular procedure to solve it. We use the intercept matrix to identify the zero valued variables of 01MKP which is known as redundant variables. In this heuristic, first the dominance property of the intercept matrix of constraints is exploited to reduce the search space to find the optimal or near optimal solutions of 01MKP, second, we improve the solution by using the pseudo-utility ratio based on surrogate constraint of 01MKP. This heuristic is tested for benchmark problems of sizes upto 2500, taken from literature and the results are compared with optimum solutions. Space and computational complexity of solving 01MKP using this approach are also presented. The encouraging results especially for relatively large size test problems indicate that this heuristic can successfully be used for finding good solutions for highly constrained NP-hard problems.

Keywords: 0-1 Multi constrained Knapsack problem, heuristic, computational complexity, NP-Hard problems.

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Authors: Miloš Šeda

Abstract:

Finding the minimal logical functions has important applications in the design of logical circuits. This task is solved by many different methods but, frequently, they are not suitable for a computer implementation. We briefly summarise the well-known Quine-McCluskey method, which gives a unique procedure of computing and thus can be simply implemented, but, even for simple examples, does not guarantee an optimal solution. Since the Petrick extension of the Quine-McCluskey method does not give a generally usable method for finding an optimum for logical functions with a high number of values, we focus on interpretation of the result of the Quine-McCluskey method and show that it represents a set covering problem that, unfortunately, is an NP-hard combinatorial problem. Therefore it must be solved by heuristic or approximation methods. We propose an approach based on genetic algorithms and show suitable parameter settings.

Keywords: Boolean algebra, Karnaugh map, Quine-McCluskey method, set covering problem, genetic algorithm.

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Authors: Amalia Suzianti

Abstract:

This research elaborates decision models for product innovation in the early phases, focusing on one of the most widely implemented method in marketing research: conjoint analysis and the related conjoint-based models with special focus on heuristics programming techniques for the development of optimal product innovation. The concept, potential, requirements and limitations of conjoint analysis and its conjoint-based heuristics successors are analysed and the development of conceptual framework of Genetic Algorithm (GA) as one of the most widely implemented heuristic methods for developing product innovations are discussed.

Keywords: Product Innovation, Conjoint Analysis, Heuristic Model, Genetic Algorithm

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Authors: Warattapop Chainate, Peeraya Thapatsuwan, Pupong Pongcharoen

Abstract:

The hybridisation of genetic algorithm with heuristics has been shown to be one of an effective way to improve its performance. In this work, genetic algorithm hybridised with four heuristics including a new heuristic called neighbourhood improvement were investigated through the classical travelling salesman problem. The experimental results showed that the proposed heuristic outperformed other heuristics both in terms of quality of the results obtained and the computational time.

Keywords: Genetic Algorithm, Hybridisation, Metaheuristics, Travelling Salesman Problem.

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Authors: Zeyu Zhang, Guisheng Yin, Ziying Zhang, Liguo Zhang

Abstract:

This paper mainly studies the path planning method based on ant colony optimization (ACO), and proposes heuristic integration ant colony optimization (HIACO). This paper not only analyzes and optimizes the principle, but also simulates and analyzes the parameters related to the application of HIACO in path planning. Compared with the original algorithm, the improved algorithm optimizes probability formula, tabu table mechanism and updating mechanism, and introduces more reasonable heuristic factors. The optimized HIACO not only draws on the excellent ideas of the original algorithm, but also solves the problems of premature convergence, convergence to the sub optimal solution and improper exploration to some extent. HIACO can be used to achieve better simulation results and achieve the desired optimization. Combined with the probability formula and update formula, several parameters of HIACO are tested. This paper proves the principle of the HIACO and gives the best parameter range in the research of path planning.

Keywords: Ant colony optimization, heuristic integration, path planning

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Authors: C. E. Nugraheni, L. Abednego

Abstract:

This paper is concerned with minimization of mean tardiness and flow time in a real single machine production scheduling problem. Two variants of genetic algorithm as metaheuristic are combined with hyper-heuristic approach are proposed to solve this problem. These methods are used to solve instances generated with real world data from a company. Encouraging results are reported.

Keywords: Hyper-heuristics, evolutionary algorithms, production scheduling.

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Authors: Norisma Idris, Sapiyan Baba, Rukaini Abdullah

Abstract:

Summarizing skills have been introduced to English syllabus in secondary school in Malaysia to evaluate student-s comprehension for a given text where it requires students to employ several strategies to produce the summary. This paper reports on our effort to develop a computer-based summarization assessment system that detects the strategies used by the students in producing their summaries. Sentence decomposition of expert-written summaries is used to analyze how experts produce their summary sentences. From the analysis, we identified seven summarizing strategies and their rules which are then transformed into a set of heuristic rules on how to determine the summarizing strategies. We developed an algorithm based on the heuristic rules and performed some experiments to evaluate and support the technique proposed.

Keywords: Summarizing strategies, heuristic rules, sentencedecomposition.

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Authors: Wahyudin P. Syam, Ibrahim M. Al-Harkan

Abstract:

This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Scheduling Problem with Parallel Machines. This problem is known to be NP-Hard. This study proposes three algorithms among improvement heuristic searches which are: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). SA and TS are known as deterministic improvement heuristic search. GA is known as stochastic improvement heuristic search. A comprehensive comparison from these three improvement heuristic searches is presented. The results for the experiments conducted show that TS is effective and efficient to solve HFS scheduling problems.

Keywords: Flow shop, genetic algorithm, simulated annealing, tabu search.

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Authors: R. Srinivasa Rao, S. V. L. Narasimham

Abstract:

This paper presents a novel approach for optimal reconfiguration of radial distribution systems. Optimal reconfiguration involves the selection of the best set of branches to be opened, one each from each loop, such that the resulting radial distribution system gets the desired performance. In this paper an algorithm is proposed based on simple heuristic rules and identified an effective switch status configuration of distribution system for the minimum loss reduction. This proposed algorithm consists of two parts; one is to determine the best switching combinations in all loops with minimum computational effort and the other is simple optimum power loss calculation of the best switching combination found in part one by load flows. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 33-bus system. The results show that the performance of the proposed method is better than that of the other methods.

Keywords: Distribution system, network reconfiguration, powerloss reduction, radial network, heuristic technique.

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Authors: Kenza Aida Amara, Bachir Djebbar

Abstract:

We treat the two-dimensional bin packing problem which involves packing a given set of rectangles into a minimum number of larger identical rectangles called bins. This combinatorial problem is NP-hard. We propose a pretreatment for the oriented version of the problem that allows the valorization of the lost areas in the bins and the reduction of the size problem. A heuristic method based on the strategy first-fit adapted to this problem is presented. We present an approach of resolution by bee colony optimization. Computational results express a comparison of the number of bins used with and without pretreatment.

Keywords: Bee colony optimization, bin packing, heuristic algorithm, pretreatment.

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Authors: Christian Meinecke, Bernd Scholz-Reiter

Abstract:

The integrated problem of production and distribution scheduling is relevant in many industrial applications. Thus, many heuristics to solve this integrated problem have been developed in the last decade. Most of these heuristics use a sequential working principal or a single decomposition and integration approach to separate and solve subproblems. A heuristic using a multi step decomposition and integration approach is presented in this paper and evaluated in a case study. The result show significant improved results compared with sequential scheduling heuristics.

Keywords: Production and outbound distribution, integrated planning, heuristic, decomposition and integration.

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Authors: Parham Azimi, Hamid Reza Charmchi

Abstract:

In this research, we have developed a new efficient heuristic algorithm for the dynamic facility layout problem with budget constraint (DFLPB). This heuristic algorithm combines two mathematical programming methods such as discrete event simulation and linear integer programming (IP) to obtain a near optimum solution. In the proposed algorithm, the non-linear model of the DFLP has been changed to a pure integer programming (PIP) model. Then, the optimal solution of the PIP model has been used in a simulation model that has been designed in a similar manner as the DFLP for determining the probability of assigning a facility to a location. After a sufficient number of runs, the simulation model obtains near optimum solutions. Finally, to verify the performance of the algorithm, several test problems have been solved. The results show that the proposed algorithm is more efficient in terms of speed and accuracy than other heuristic algorithms presented in previous works found in the literature.

Keywords: Budget constraint, Dynamic facility layout problem, Integer programming, Simulation

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Authors: S. Esfandeh, M. Sedighizadeh

Abstract:

Weather systems use enormously complex combinations of numerical tools for study and forecasting. Unfortunately, due to phenomena in the world climate, such as the greenhouse effect, classical models may become insufficient mostly because they lack adaptation. Therefore, the weather forecast problem is matched for heuristic approaches, such as Evolutionary Algorithms. Experimentation with heuristic methods like Particle Swarm Optimization (PSO) algorithm can lead to the development of new insights or promising models that can be fine tuned with more focused techniques. This paper describes a PSO approach for analysis and prediction of data and provides experimental results of the aforementioned method on realworld meteorological time series.

Keywords: Weather, Climate, PSO, Prediction, Meteorological

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Authors: D.Venkatesan, K.Kannan, S. Raja Balachandar

Abstract:

In this paper a new Genetic Algorithm based on a heuristic operator and Centre of Mass selection operator (CMGA) is designed for the unbounded knapsack problem(UKP), which is NP-Hard combinatorial optimization problem. The proposed genetic algorithm is based on a heuristic operator, which utilizes problem specific knowledge. This center of mass operator when combined with other Genetic Operators forms a competitive algorithm to the existing ones. Computational results show that the proposed algorithm is capable of obtaining high quality solutions for problems of standard randomly generated knapsack instances. Comparative study of CMGA with simple GA in terms of results for unbounded knapsack instances of size up to 200 show the superiority of CMGA. Thus CMGA is an efficient tool of solving UKP and this algorithm is competitive with other Genetic Algorithms also.

Keywords: Genetic Algorithm, Unbounded Knapsack Problem, Combinatorial Optimization, Meta-Heuristic, Center of Mass

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Authors: Mahmoud M. Othman, Y. G. Hegazy, A. Y. Abdelaziz

Abstract:

This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm. 

Keywords: Distributed generation, heuristic approach, Optimization, planning.

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Authors: Mahmoud M. Othman, Yasser G. Hegazy

Abstract:

This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm.

Keywords: Distributed generation, heuristic approach, Optimization, planning.

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Authors: Adel Bouchakhchoukha, Hakim Akeb

Abstract:

In this work, we propose a hybrid heuristic in order to solve the Team Orienteering Problem (TOP). Given a set of points (or customers), each with associated score (profit or benefit), and a team that has a fixed number of members, the problem to solve is to visit a subset of points in order to maximize the total collected score. Each member performs a tour starting at the start point, visiting distinct customers and the tour terminates at the arrival point. In addition, each point is visited at most once, and the total time in each tour cannot be greater than a given value. The proposed heuristic combines beam search and a local optimization strategy. The algorithm was tested on several sets of instances and encouraging results were obtained.

Keywords: Team Orienteering Problem, Vehicle Routing, Beam Search, Local Search.

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