Search results for: combinatorial%20optimization%20operator

Authors: Y. Wang

Abstract:

The Traveling salesman problem (TSP) is NP-hard in combinatorial optimization. The research shows the algorithms for TSP on the sparse graphs have the shorter computation time than those for TSP according to the complete graphs. We present an improved iterative algorithm to compute the sparse graphs for TSP by frequency graphs computed with frequency quadrilaterals. The iterative algorithm is enhanced by adjusting two parameters of the algorithm. The computation time of the algorithm is O(CN_maxn²) where C is the iterations, N_max is the maximum number of frequency quadrilaterals containing each edge and n is the scale of TSP. The experimental results showed the computed sparse graphs generally have less than 5n edges for most of these Euclidean instances. Moreover, the maximum degree and minimum degree of the vertices in the sparse graphs do not have much difference. Thus, the computation time of the methods to resolve the TSP on these sparse graphs will be greatly reduced.

Keywords: frequency quadrilateral, iterative algorithm, sparse graph, traveling salesman problem

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Authors: Wuthichai Wongthatsanekorn, Nuntana Matheekrieangkrai

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This research proposes Bee Algorithm (BA) to optimize Ready Mixed Concrete (RMC) truck scheduling problem from single batch plant to multiple construction sites. This problem is considered as an NP-hard constrained combinatorial optimization problem. This paper provides the details of the RMC dispatching process and its related constraints. BA was then developed to minimize total waiting time of RMC trucks while satisfying all constraints. The performance of BA is then evaluated on two benchmark problems (3 and 5construction sites) according to previous researchers. The simulation results of BA are compared in term of efficiency and accuracy with Genetic Algorithm (GA) and all problems show that BA approach outperforms GA in term of efficiency and accuracy to obtain optimal solution. Hence, BA approach could be practically implemented to obtain the best schedule.

Keywords: bee colony optimization, ready mixed concrete problem, ruck scheduling, multiple construction sites

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Authors: Joselito Medina-Marin, Alexandr Karelin, Ana Tarasenko, Juan Carlos Seck-Tuoh-Mora, Norberto Hernandez-Romero, Eva Selene Hernandez-Gress

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A production line designer faces with several challenges in manufacturing system design. One of them is the assignment of buffer slots in between every machine of the production line in order to maximize the throughput of the whole line, which is known as the Buffer Allocation Problem (BAP). The BAP is a combinatorial problem that depends on the number of machines and the total number of slots to be distributed on the production line. In this paper, we are proposing a Petri Net (PN) Model to obtain the throughput in unreliable production lines, based on PN mathematical tools and the decomposition method. The results obtained by this methodology are similar to those presented in previous works, and the number of machines is not a hard restriction.

Keywords: buffer allocation problem, Petri Nets, throughput, production lines

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Authors: Michele Aleandri, Matteo Colangeli, Davide Gabrielli

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We study a generalization to a continuous setting of the classical Markov chain tree theorem. In particular, we consider an irreducible diffusion process on a metric graph. The unique invariant measure has an atomic component on the vertices and an absolutely continuous part on the edges. We show that the corresponding density at x can be represented by a normalized superposition of the weights associated to metric arborescences oriented toward the point x. A metric arborescence is a metric tree oriented towards its root. The weight of each oriented metric arborescence is obtained by the product of the exponential of integrals of the form ∫a/b², where b is the drift and σ² is the diffusion coefficient, along the oriented edges, for a weight for each node determined by the local orientation of the arborescence around the node and for the inverse of the diffusion coefficient at x. The metric arborescences are obtained by cutting the original metric graph along some edges.

Keywords: diffusion processes, metric graphs, invariant measure, reversibility

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Authors: Mohsen Ziaee

Abstract:

In this paper, a mixed integer linear programming (MILP) model is presented to solve the flexible job shop scheduling problem (FJSP). This problem is one of the hardest combinatorial problems. The objective considered is the minimization of the makespan. The computational results of the proposed MILP model were compared with those of the best known mathematical model in the literature in terms of the computational time. The results show that our model has better performance with respect to all the considered performance measures including relative percentage deviation (RPD) value, number of constraints, and total number of variables. By this improved mathematical model, larger FJS problems can be optimally solved in reasonable time, and therefore, the model would be a better tool for the performance evaluation of the approximation algorithms developed for the problem.

Keywords: scheduling, flexible job shop, makespan, mixed integer linear programming

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Authors: Elham Kazemi

Abstract:

Quadratic Assignment Problem (QAP) is one the combinatorial optimization problems about which research has been done in many companies for allocating some facilities to some locations. The issue of particular importance in this process is the costs of this allocation and the attempt in this problem is to minimize this group of costs. Since the QAP’s are from NP-hard problem, they cannot be solved by exact solution methods. Cuckoo Optimization Algorithm is a Meta-heuristicmethod which has higher capability to find the global optimal points. It is an algorithm which is basically raised to search a continuous space. The Quadratic Assignment Problem is the issue which can be solved in the discrete space, thus the standard arithmetic operators of Cuckoo Optimization Algorithm need to be redefined on the discrete space in order to apply the Cuckoo Optimization Algorithm on the discrete searching space. This paper represents the way of discretizing the Cuckoo optimization algorithm for solving the quadratic assignment problem.

Keywords: Quadratic Assignment Problem (QAP), Discrete Cuckoo Optimization Algorithm (DCOA), meta-heuristic algorithms, optimization algorithms

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Authors: Samia Kouki

Abstract:

Solving NP-hard combinatorial optimization problems by exact search methods, such as Branch-and-Bound, may degenerate to complete enumeration. For that reason, exact approaches limit us to solve only small or moderate size problem instances, due to the exponential increase in CPU time when problem size increases. One of the most promising ways to reduce significantly the computational burden of sequential versions of Branch-and-Bound is to design parallel versions of these algorithms which employ several processors. This paper describes a parallel Branch-and-Bound algorithm called GALB for solving the classical permutation flowshop scheduling problem as well as its implementation on a Grid computing infrastructure. The experimental study of our distributed parallel algorithm gives promising results and shows clearly the benefit of the parallel paradigm to solve large-scale instances in moderate CPU time.

Keywords: grid computing, permutation flow shop problem, branch and bound, load balancing

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Authors: Yassir AbdelRazig

Abstract:

Evolutionary optimization methods such as genetic algorithms have been used extensively for the construction site layout problem. More recently, ant colony optimization algorithms, which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to benchmark combinatorial optimization problems. This paper proposes a formulation of the site layout problem in terms of a sequencing problem that is suitable for solution using an ant colony optimization algorithm. In the construction industry, site layout is a very important planning problem. The objective of site layout is to position temporary facilities both geographically and at the correct time such that the construction work can be performed satisfactorily with minimal costs and improved safety and working environment. During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the construction site layout problem. This paper proposes an ant colony optimization model for construction site layout. A simple case study for a highway project is utilized to illustrate the application of the model.

Keywords: ant colony, construction site layout, optimization, genetic algorithms

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Authors: Seyed Mehran Kazemi, Bahare Fatemi

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Sudoku is a logic-based combinatorial puzzle game which is popular among people of different ages. Due to this popularity, computer softwares are being developed to generate and solve Sudoku puzzles with different levels of difficulty. Several methods and algorithms have been proposed and used in different softwares to efficiently solve Sudoku puzzles. Various search methods such as stochastic local search have been applied to this problem. Genetic Algorithm (GA) is one of the algorithms which have been applied to this problem in different forms and in several works in the literature. In these works, chromosomes with little or no information were considered and obtained results were not promising. In this paper, we propose a new way of applying GA to this problem which uses more-informed chromosomes than other works in the literature. We optimize the parameters of our GA using puzzles with different levels of difficulty. Then we use the optimized values of the parameters to solve various puzzles and compare our results to another GA-based method for solving Sudoku puzzles.

Keywords: genetic algorithm, optimization, solving Sudoku puzzles, stochastic local search

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Authors: Regita P. Permata, Ulfa S. Nuraini

Abstract:

Traveling Salesman Problem (TSP) is the most studied problem in combinatorial optimization. In simple language, TSP can be described as a problem of finding a minimum distance tour to a city, starting and ending in the same city, and exactly visiting another city. In product distribution, companies often get problems in determining the minimum distance that affects the time allocation. In this research, we aim to apply TSP heuristic methods to simulate nodes as city coordinates in product distribution. The heuristics used are sub tour reversal, nearest neighbor, farthest insertion, cheapest insertion, nearest insertion, and arbitrary insertion. We have done simulation nodes using Euclidean distances to compare the number of cities and processing time, thus we get optimum heuristic method. The results show that the optimum heuristic methods are farthest insertion and nearest insertion. These two methods can be recommended to solve product distribution problems in certain companies.

Keywords: Euclidean, heuristics, simulation, TSP

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Authors: A. S. Mousa, R. I. Rajab, A. A. Pinto

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An envy behavioral game theoretical model with two types of homogeneous players is considered in this paper. The strategy space of each type of players is a discrete set with only two alternatives. The preferences of each type of players is given by a discrete utility function. All envy strategies that form Nash equilibria and the corresponding envy Nash domains for each type of players have been characterized. We use geometry to construct two dimensional envy tilings where the horizontal axis reflects the preference for players of type one, while the vertical axis reflects the preference for the players of type two. The influence of the envy behavior parameters on the Cartesian position of the equilibria has been studied, and in each envy tiling we determine the envy Nash equilibria. We observe that there are 1024 combinatorial classes of envy tilings generated from envy chromosomes: 256 of them are being structurally stable while 768 are with bifurcation. Finally, some conditions for the disparate envy Nash equilibria are stated.

Keywords: game theory, Nash equilibrium, envy Nash behavior, geometric tilings, bifurcation thresholds

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Authors: Ziwei Xu

Abstract:

The increasing emphasis on mobility has driven the application of innovative communication technologies across various industries. In the retail sector, Near Field Communication (NFC) has emerged as a significant and transformative technology, particularly in the payment and retail supermarket sectors. NFC enables new payment methods, such as electronic wallets, and enhances information management in supermarkets, contributing to the growth of the trade. This report presents a comprehensive analysis of NFC technology, focusing on five key aspects. Firstly, it provides an overview of NFC, including its application methods and development history. Additionally, it incorporates Arthur's work on combinatorial evolution to elucidate the emergence and impact of NFC technology, while acknowledging the limitations of the model in analyzing NFC. The report then summarizes the positive influence of NFC on the retail industry along with its associated constraints. Furthermore, it explores the adoption of NFC from both organizational and individual perspectives, employing the Best Predictors of organizational IT adoption and UTAUT2 models, respectively. Finally, the report discusses the potential future replacement of NFC with biometrics technology, highlighting its advantages over NFC and leveraging Arthur's model to investigate its future development prospects.

Keywords: innovation, NFC, industry, biometrics

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a new hybrid particle swarm optimization algorithm is proposed for the multi-objective flexible job shop scheduling problem that is very important and hard combinatorial problem. The Pareto approach is used for solving the multi-objective problem. Several new local search heuristics are integrated into an algorithm based on the critical block concept to enhance the performance of the algorithm. The algorithm is compared with the recently published multi-objective algorithms based on benchmarks selected from the literature. Several metrics are used for quantifying performance and comparison of the achieved solutions. The algorithms are also compared based on the Weighting summation of objectives approach. The proposed algorithm can find the Pareto solutions more efficiently than the compared algorithms in less computational time.

Keywords: swarm-based optimization, local search, Pareto optimality, flexible job shop scheduling, multi-objective optimization

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Authors: Selcuk Aslan, Dervis Karaboga, Celal Ozturk

Abstract:

Artificial Bee Colony (ABC) algorithm is one of the most successful swarm intelligence based metaheuristics. It has been applied to a number of constrained or unconstrained numerical and combinatorial optimization problems. In this paper, we presented a parallelized version of ABC algorithm by adapting employed and onlooker bee phases to the Compute Unified Device Architecture (CUDA) platform which is a graphical processing unit (GPU) programming environment by NVIDIA. The execution speed and obtained results of the proposed approach and sequential version of ABC algorithm are compared on functions that are typically used as benchmarks for optimization algorithms. Tests on standard benchmark functions with different colony size and number of parameters showed that proposed parallelization approach for ABC algorithm decreases the execution time consumed by the employed and onlooker bee phases in total and achieved similar or better quality of the results compared to the standard sequential implementation of the ABC algorithm.

Keywords: Artificial Bee Colony algorithm, GPU computing, swarm intelligence, parallelization

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Authors: H. Mousavi, M. Sharifi, H. Pourvaziri

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Redundancy Allocation Problem (RAP) is a well-known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. In this paper, to be more practical, we have considered the problem of redundancy allocation of series system with interval valued reliability of components. Therefore, during the search process, the reliabilities of the components are considered as a stochastic variable with a lower and upper bounds. In order to optimize the problem, we proposed a simulated annealing based on cloud theory (CBSAA). Also, the Monte Carlo simulation (MCS) is embedded to the CBSAA to handle the random variable components’ reliability. This novel approach has been investigated by numerical examples and the experimental results have shown that the CBSAA combining MCS is an efficient tool to solve the RAP of systems with interval-valued component reliabilities.

Keywords: redundancy allocation problem, simulated annealing, cloud theory, monte carlo simulation

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Authors: Vadim E. Levit, Eugen Mandrescu

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A graph is well-covered if all its maximal independent sets are of the same size. A well-covered graph is 1-well-covered if deletion of every vertex of the graph leaves it well-covered. It is known that a graph without isolated vertices is 1-well-covered if and only if every two disjoint independent sets are included in two disjoint maximum independent sets. Well-covered graphs are related to combinatorial commutative algebra (e.g., every Cohen-Macaulay graph is well-covered, while each Gorenstein graph without isolated vertices is 1-well-covered). Our intent is to construct several infinite families of 1-well-covered graphs using the following known graph operations: corona, join, and rooted product of graphs. Adopting some known techniques used to advantage for well-covered graphs, one can prove that: if the graph G has no isolated vertices, then the corona of G and H is 1-well-covered if and only if H is a complete graph of order two at least; the join of the graphs G and H is 1-well-covered if and only if G and H have the same independence number and both are 1-well-covered; if H satisfies the property that every three pairwise disjoint independent sets are included in three pairwise disjoint maximum independent sets, then the rooted product of G and H is 1-well-covered, for every graph G. These findings show not only how to generate some more families of 1-well-covered graphs, but also that, to this aim, sometimes, one may use graphs that are not necessarily 1-well-covered.

Keywords: maximum independent set, corona, concatenation, join, well-covered graph

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Authors: Qasim Zafar, Matthew Anderson, Esteban Garcia, Steven Drager

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Software failures can present an enormous detriment to people's lives and cost millions of dollars to repair when they are unexpectedly encountered in the wild. Despite a significant portion of the software development lifecycle and resources are dedicated to testing, software failures are a relatively frequent occurrence. Nevertheless, the evaluation of testing effectiveness remains at the forefront of ensuring high-quality software and software metrics play a critical role in providing valuable insights into quantifiable objectives to assess the level of assurance and confidence in the system. As the selection of appropriate metrics can be an arduous process, the goal of this paper is to shed light on the significance of software metrics by examining a range of testing techniques and metrics as well as identifying key areas for improvement. Additionally, through this investigation, readers will gain a deeper understanding of how metrics can help to drive informed decision-making on delivering high-quality software and facilitate continuous improvement in testing practices.

Keywords: software testing, software metrics, testing effectiveness, black box testing, random testing, adaptive random testing, combinatorial testing, fuzz testing, equivalence partition, boundary value analysis, white box testing

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Authors: Raed Alnajjar, Mohd Zakree, Ahmad Nazri

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In this study, we apply Discrete Group Search Optimizer (DGSO) for solving Traveling Salesman Problem (TSP). The DGSO is a nature inspired optimization algorithm that imitates the animal behavior, especially animal searching behavior. The proposed DGSO uses a vector representation and some discrete operators, such as destruction, construction, differential evolution, swap and insert. The TSP is a well-known hard combinatorial optimization problem, which seeks to find the shortest path among numbers of cities. The performance of the proposed DGSO is evaluated and tested on benchmark instances which listed in LIBTSP dataset. The experimental results show that the performance of the proposed DGSO is comparable with the other methods in the state of the art for some instances. The results show that DGSO outperform Ant Colony System (ACS) in some instances whilst outperform other metaheuristic in most instances. In addition to that, the new results obtained a number of optimal solutions and some best known results. DGSO was able to obtain feasible and good quality solution across all dataset.

Keywords: discrete group search optimizer (DGSO); Travelling salesman problem (TSP); Variable neighborhood search(VNS)

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Authors: Bahare Fatemi, Seyed Mehran Kazemi, Nazanin Mehrasa

Abstract:

Sudoku is a logic-based combinatorial puzzle game which people in different ages enjoy playing it. The challenging and addictive nature of this game has made it a ubiquitous game. Most magazines, newspapers, puzzle books, etc. publish lots of Sudoku puzzles every day. These puzzles often come in different levels of difficulty so that all people, from beginner to expert, can play the game and enjoy it. Generating puzzles with different levels of difficulty is a major concern of Sudoku designers. There are several works in the literature which propose ways of generating puzzles having a desirable level of difficulty. In this paper, we propose a method based on constraint satisfaction problems to evaluate the difficulty of the Sudoku puzzles. Then, we propose a hill climbing method to generate puzzles with different levels of difficulty. Whereas other methods are usually capable of generating puzzles with only few number of difficulty levels, our method can be used to generate puzzles with arbitrary number of different difficulty levels. We test our method by generating puzzles with different levels of difficulty and having a group of 15 people solve all the puzzles and recording the time they spend for each puzzle.

Keywords: constraint satisfaction problem, generating Sudoku puzzles, hill climbing

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Authors: Nader A. Al Theeb, Zaid Abu Manneh, Ibrahim Al-Qadi

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Traveling salesman problem (TSP) is a combinatorial integer optimization problem that asks "What is the optimal route for a vehicle to traverse in order to deliver requests to a given set of customers?”. It is widely used by the package carrier companies’ distribution centers. The main goal of applying the TSP in courier organizations is to minimize the time that it takes for the courier in each trip to deliver or pick up the shipments during a day. In this article, an optimization model is constructed to create a new TSP variant to optimize the routing in a courier organization with a consideration of congestion in Amman, the capital of Jordan. Real data were collected by different methods and analyzed. Then, concert technology - CPLEX was used to solve the proposed model for some random generated data instances and for the real collected data. At the end, results have shown a great improvement in time compared with the current trip times, and an economic study was conducted afterwards to figure out the impact of using such models.

Keywords: travel salesman problem, congestions, pick-up, integer programming, package carriers, service engineering

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Authors: Chaghoub Soraya, Zhang Xiaoyan

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This research presents the first constant approximation algorithm to the p-median network design problem with multiple cable types. This problem was addressed with a single cable type and there is a bifactor approximation algorithm for the problem. To the best of our knowledge, the algorithm proposed in this paper is the first constant approximation algorithm for the p-median network design with multiple cable types. The addressed problem is a combination of two well studied problems which are p-median problem and network design problem. The introduced algorithm is a random sampling approximation algorithm of constant factor which is conceived by using some random sampling techniques form the literature. It is based on a redistribution Lemma from the literature and a steiner tree problem as a subproblem. This algorithm is simple, and it relies on the notions of random sampling and probability. The proposed approach gives an approximation solution with one constant ratio without violating any of the constraints, in contrast to the one proposed in the literature. This paper provides a (21 + 2)-approximation algorithm for the p-median network design problem with multiple cable types using random sampling techniques.

Keywords: approximation algorithms, buy-at-bulk, combinatorial optimization, network design, p-median

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, José J. Hernández-Flores, Edith Olaco Garcia

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The Project Scheduling Problem (PSP) is a generic name given to a whole class of problems in which the best form, time, resources and costs for project scheduling are necessary. The PSP is an application area related to the project management. This paper aims at being a guide to understand PSP by presenting a survey of the general parameters of PSP: the Resources (those elements that realize the activities of a project), and the Activities (set of operations or own tasks of a person or organization); the mathematical models of the main variants of PSP and the algorithms used to solve the variants of the PSP. The project scheduling is an important task in project management. This paper contains mathematical models, resources, activities, and algorithms of project scheduling problems. The project scheduling problem has attracted researchers of the automotive industry, steel manufacturer, medical research, pharmaceutical research, telecommunication, industry, aviation industry, development of the software, manufacturing management, innovation and technology management, construction industry, government project management, financial services, machine scheduling, transportation management, and others. The project managers need to finish a project with the minimum cost and the maximum quality.

Keywords: PSP, Combinatorial Optimization Problems, Project Management; Manufacturing Management, Technology Management.

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Authors: Sopheak Sorn, Kwok Yip Szeto

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Reliability is one of the important measures of how well the system meets its design objective, and mathematically is the probability that a complex system will perform satisfactorily. When the system is described by a network of N components (nodes) and their L connection (links), the reliability of the system becomes a network design problem that is an NP-hard combinatorial optimization problem. In this paper, we address the network design problem for a random point set’s pattern in two dimensions. We make use of a Voronoi construction with each cell containing exactly one point in the point pattern and compute the reliability of the Voronoi’s dual, i.e. the Delaunay graph. We further investigate the communicability of the Delaunay network. We find that there is a positive correlation and a negative correlation between the homogeneity of a Delaunay's degree distribution with its reliability and its communicability respectively. Based on the correlations, we alter the communicability and the reliability by performing random edge flips, which preserve the number of links and nodes in the network but can increase the communicability in a Delaunay network at the cost of its reliability. This transformation is later used to optimize a Delaunay network with the optimum geometric mean between communicability and reliability. We also discuss the importance of the edge flips in the evolution of real soap froth in two dimensions.

Keywords: Communicability, Delaunay triangulation, Edge Flip, Reliability, Two dimensional network, Voronio

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Authors: P. Parthiban, Sonu Rajak, N. Kannan, R. Dhanalakshmi

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This paper deals with the Vehicle Routing Problem (VRP) with environmental considerations which is called Pollution Routing Problem (PRP). The objective is to minimize the operational and environmental costs. It consists of routing a number of vehicles to serve a set of customers, and determining fuel consumption, driver wages and their speed on each route segment, while respecting the capacity constraints and time windows. In this context, we presented an Ant Colony Optimization (ACO) approach, combined with a Speed Optimization Algorithm (SOA) to solve the PRP. The proposed solution method consists of two stages. Stage one is to solve a Vehicle Routing Problem with Time Window (VRPTW) using ACO and in the second stage a SOA is run on the resulting VRPTW solutions. Given a vehicle route, the SOA consists of finding the optimal speed on each arc of the route in order to minimize an objective function comprising fuel consumption costs and driver wages. The proposed algorithm tested on benchmark problem, the preliminary results show that the proposed algorithm is able to provide good solutions.

Keywords: ant colony optimization, CO2 emissions, combinatorial optimization, speed optimization, vehicle routing

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

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One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: rough set theory, attribute reduction, fuzzy logic, memetic algorithms, record to record algorithm, great deluge algorithm

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Authors: Bezhan Ghvaberidze

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A fuzzy multi-objective facility location-selection problem (FLSP) under uncertain and extreme environments based on possibility theory is developed. The model’s uncertain parameters in the q-rung orthopair fuzzy values are presented and transformed in the Dempster-Shaper’s belief structure environment. An objective function – distribution centers’ selection ranking index as an extension of Dempster’s extremal expectations under discrimination q-rung orthopair fuzzy information is constructed. Experts evaluate each humanitarian aid from distribution centers (HADC) against each of the uncertain factors. HADCs location problem is reduced to the bicriteria problem of partitioning the set of customers by the set of centers: (1) – Minimization of transportation costs; (2) – Maximization of centers’ selection ranking indexes. Partitioning type constraints are also constructed. For an illustration of the obtained results, a numerical example is created from the facility location-selection problem.

Keywords: FLSP, multi-objective combinatorial optimization problem, evidence theory, HADC, q-rung orthopair fuzzy set, possibility theory

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Authors: Ernesto Linan, Linda Cruz, Lucero Becerra

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In this paper, a new hybridized algorithm based on Chemical Reaction Optimization and Simulated Annealing is proposed to solve the alignment sequence Problem. The Chemical Reaction Optimization is a population-based meta-heuristic algorithm based on the principles of a chemical reaction. Simulated Annealing is applied to solve a large number of combinatorial optimization problems of general-purpose. In this paper, we propose hybridization between Chemical Reaction Optimization algorithm and Simulated Annealing in order to solve the Sequence Alignment Problem. An initial population of molecules is defined at beginning of the proposed algorithm, where each molecule represents a sequence alignment problem. In order to simulate inter-molecule collisions, the process of Chemical Reaction is placed inside the Metropolis Cycle at certain values of temperature. Inside this cycle, change of molecules is done due to collisions; some molecules are accepted by applying Boltzmann probability. The results with the hybrid scheme are better than the results obtained separately.

Keywords: chemical reaction optimization, sequence alignment problem, simulated annealing algorithm, metaheuristics

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Authors: Hala Zaghloul, Taymoor Nazmy

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One of the great challenges of the 21st century is to build a robot that can perceive and act within its environment and communicate with people, while also exhibiting the cognitive capabilities that lead to performance like that of people. The Pulse Coupled Neural Network, PCNN, is a relative new ANN model that derived from a neural mammal model with a great potential in the area of image processing as well as target recognition, feature extraction, speech recognition, combinatorial optimization, compressed encoding. PCNN has unique feature among other types of neural network, which make it a candid to be an important approach for perceiving in cognitive systems. This work show and emphasis on the potentials of PCNN to perform different tasks related to image processing. The main drawback or the obstacle that prevent the direct implementation of such technique, is the need to find away to control the PCNN parameters toward perform a specific task. This paper will evaluate the performance of PCNN standard model for processing images with different properties, and select the important parameters that give a significant result, also, the approaches towards find a way for the adaptation of the PCNN parameters to perform a specific task.

Keywords: cognitive system, image processing, segmentation, PCNN kernels

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Authors: Antonio Breda d’Azevedo

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Regularity has often been present in the form of regular polyhedra or tessellations; classical examples are the nine regular polyhedra consisting of the five Platonic solids (regular convex polyhedra) and the four Kleper-Poinsot polyhedra. These polytopes can be seen as regular maps. Maps are cellular embeddings of graphs (with possibly multiple edges, loops or dangling edges) on compact connected (closed) surfaces with or without boundary. The n-dimensional abstract polytopes, particularly the regular ones, have gained popularity over recent years. The main focus of research has been their symmetries and regularity. Planification of polyhedra helps its spatial construction, yet it destroys its symmetries. To our knowledge there is no “planification” for n-dimensional polytopes. However we show that it is possible to make a “surfacification” of the n-dimensional polytope, that is, it is possible to construct a restrictedly-marked map representation of the abstract polytope on some surface that describes its combinatorial structures as well as all of its symmetries. We also show that there are infinitely many ways to do this; yet there is one that is more natural that describes reflections on the sides ((n−1)-faces) of n-simplices with reflections on the sides of n-polygons. We illustrate this construction with the 4-tetrahedron (a regular 4-polytope with automorphism group of size 120) and the 4-cube (a regular 4-polytope with automorphism group of size 384).

Keywords: abstract polytope, automorphism group, N-simplicies, symmetry

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Authors: Kuladip Jana

Abstract:

Benzo(a)pyrene [B(a)P] is an environmental toxicant present mostly in cigarette smoke and car exhaust, is an aryl hydrocarbon receptor (AhR) ligand that exerts its toxic effects on both male and female reproductive systems. In this study, the effect of B(a)P at different doses (0.1, 0.25, 0.5, 1 and 5 mg /kg body weight) was studied on male reproductive system of rat. A significant decrease in cauda epididymal sperm count and motility along with the presence of sperm head abnormalities and altered epididymal and testicular histology were documented following B(a)P treatment. B(a)P treatment resulted apoptotic sperm cells as observed by TUNEL and Annexin V-PI assay with increased ROS, altered sperm mitochondrial membrane potential (ΔΨm) with a simultaneous decrease in the activity of antioxidant enzymes and GSH status. TUNEL positive apoptotic cells also observed in testis as well as isolated germ and Leydig cells following B(a)P exposure. Western Blot analysis revealed the activation of p38MAPK, cytosolic translocation of cytochrome-c, up-regulation of Bax and inducible nitric oxide synthase (iNOS) with cleavage of PARP and down-regulation of BCl2 in testis upon B(a)P treatment. The protein and mRNA levels of testicular key steroidogenesis regulatory proteins like StAR, cytochrome P450 IIA1 (CYPIIA1), 3β HSD, 17β HSD showed a significant decrease in a dose dependent manner while an increase in the expression of cytochrome P450 1A1 (CYP1A1), Aryl hydrocarbon Receptor (AhR), active caspase- 9 and caspase- 3 following B(a)P exposure. We conclude that exposure of benzo(a)pyrene caused testicular gamatogenic and steroidogenic disorders by induction of oxidative stress, inhibition of StAR and other steroidogenic enzymes along with activation of p38MAPK and initiated caspase-3 mediated germ and Leydig cell apoptosis.The possible protective role of naturally occurring phytochemicals against B(a)P induced testicular toxicity needs immediate consideration. Curcumin and resveratrol separately were found to protect against B(a)P induced germ cell apoptosis, and their combinatorial effect was more significant. Our present study in isolated testicular germ cell population from adult male Wistar rats, highlighted their synergistic protective effect against B(a)P induced germ cell apoptosis. Curcumin-resveratrol co-treatment decreased the expression of pro-apoptotic proteins like cleaved caspase 3,8,9, cleaved PARP, Apaf1, FasL, tBid. Curcumin-resveratrol co-treatment decreased Bax/Bcl2 ratio, mitochondria to cytosolic translocation of cytochrome c and activated the survival protein Akt. Curcumin-resveratrol decreased the expression of p53 dependent apoptotic genes like Fas, FasL, Bax, Bcl2, Apaf1.Curcumin-resveratrol co-treatment thus prevented B(a)P induced germ cell apoptosis. B(a)P induced testicular ROS generation and oxidative stress were significantly ameliorated with curcumin and resveratrol. Curcumin-resveratrol co-treatment prevented B(a)P induced nuclear translocation of AhR and CYP1A1 production. The combinatorial treatment significantly inhibited B(a)P induced ERK 1/2, p38 MAPK and JNK 1/2 activation. B(a)P treatment increased the expression of p53 and its phosphorylation (p53 ser 15). Curcumin-resveratrol co-treatment significantly decreased p53 level and its phosphorylation (p53 ser 15). The study concludes that curcumin-resveratrol synergistically modulated MAPKs and p53, prevented oxidative stress, regulated the expression of pro and anti-apoptotic proteins as well as the proteins involved in B(a)P metabolism thus protected germ cells from B(a)P induced apoptosis.

Keywords: benzo(a)pyrene, germ cell, apoptosis, oxidative stress, resveratrol, curcumin

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