Showing papers on "Extremal optimization published in 2006"
TL;DR: The effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints, is demonstrated.
Abstract: In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints.
251 citations
24 Sep 2006
TL;DR: The simulation experiment results of some complex functions optimization indicate that SWOA can enhance the diversity of the population, avoid the prematurity and GA deceptive problem to some extent, and have the high convergence speed.
Abstract: Inspired by the mechanism of small-world phenomenon, some small-world optimization operators, mainly including the local short-range searching operator and random long-range searching operator, are constructed in this paper. And a new optimization algorithm, Small-World Optimization Algo-rithm (SWOA) is explored. Compared with the corresponding Genetic Algorithms (GAs), the simulation experiment results of some complex functions optimization indicate that SWOA can enhance the diversity of the population, avoid the prematurity and GA deceptive problem to some extent, and have the high convergence speed. SWOA is shown to be an effective strategy to solve complex tasks.
150 citations
TL;DR: A heuristic rule, the smallest position value (SPV) rule, borrowed from the random key representation in genetic algorithms, is developed to enable the continuous particle swarm optimization and differential evolution algorithms to be applied to all permutation types of discrete combinatorial optimization problems.
Abstract: In this paper we present two recent metaheuristics, particle swarm optimization and differential evolution algorithms, to solve the single machine total weighted tardiness problem, which is a typical discrete combinatorial optimization problem. Most of the literature on both algorithms is concerned with continuous optimization problems, while a few deal with discrete combinatorial optimization problems. A heuristic rule, the smallest position value (SPV) rule, borrowed from the random key representation in genetic algorithms, is developed to enable the continuous particle swarm optimization and differential evolution algorithms to be applied to all permutation types of discrete combinatorial optimization problems. The performance of these two recent population based algorithms is evaluated on widely used benchmarks from the OR library. The computational results show that both algorithms show promise in solving permutation problems. In addition, a simple but very efficient local search method based on the ...
92 citations
30 Aug 2006
TL;DR: The paper makes the attempt to show how the ant colony optimization (ACO) can be applied to the MTSP with ability constraint, and shows that the proposed algorithm can find competitive solutions even not all of the best solutions within rational time, especially for large scale problems.
Abstract: Multiple travelling salesman problem (MTSP) is a typical computationally complex combinatorial optimization problem, which is an extension of the famous travelling salesman problem (TSP). The MTSP can be generalized to a wide variety of routing and scheduling problems. It is known that classical optimization procedures are not adequate for this problem. The paper makes the attempt to show how the ant colony optimization (ACO) can be applied to the MTSP with ability constraint. In this paper, we compare it with MGA by testing several standard problems from TSPLIB. The computational results show that the proposed algorithm can find competitive solutions even not all of the best solutions within rational time, especially for large scale problems
88 citations
TL;DR: A new distributed image segmentation algorithm structured as a multiagent system composed of a set of segmentation agents and a coordinator agent that ensures its convergence to an optimal segmentation as it is shown through some experimental results.
56 citations
03 Nov 2006
TL;DR: A novel EO strategy with population based search is developed, which is more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation.
Abstract: Recently, a local-search heuristic algorithm called Extremal Optimization (EO) has been successfully applied in some combinatorial optimization problems. This paper presents the studies on the applications of EO to numerical constrained optimization problems with a set of popular benchmark problems. To enhance and improve the search performance and efficiency of EO, we developed a novel EO strategy with population based search. The newly developed EO algorithm is named population-based EO (PEO). Additionally, we adopted the adaptive Levy mutation, which is more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Compared with three state-of-the-art stochastic search methods with six popular bench-mark problems, it has been shown that our approach is a good alternative to deal with the numerical constrained optimization problems.
38 citations
TL;DR: An ant colony optimization approach to the orienteering problem, a general version of the well-known traveling salesman problem with many relevant applications in industry, is developed which result in a method that is convincingly shown to be the best heuristic published for this problem class.
Abstract: This paper develops an ant colony optimization approach to the orienteering problem, a general version of the well-known traveling salesman problem with many relevant applications in industry. Based on mainstream ant colony ideas, an unusual sequenced local search and a distance based penalty function are added which result in a method that is convincingly shown to be the best heuristic published for this problem class. Results on 67 test problems show that the ant colony method performs as well or better than all other methods from the literature in all cases and does so at very modest computational cost. Furthermore, the ant colony method is insensitive to seed, problem instance, problem size and degree of constraint.
35 citations
01 Jan 2006
TL;DR: In this chapter, combinatorial issues occurring in the implementation of multicast routing, including multicast tree construction, minimization of the total message delay, center-based routing, and multicast message packing are discussed.
Abstract: Multicasting is a technique for data routing in networks that allows multiple destinations to be addressed simultaneously. The implementation of multicasting requires, however, the solution of difficult combinatorial optimization problems. In this chapter, we discuss combinatorial issues occurring in the implementation of multicast routing, including multicast tree construction, minimization of the total message delay, center-based routing, and multicast message packing. Optimization methods for these problems are discussed and the corresponding literature reviewed. Mathematical programming as well as graph models for these problems are discussed.
34 citations
06 Dec 2006
TL;DR: A new effective optimization algorithm called Genetical Swarm Optimization (GSO) is presented, an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures.
Abstract: UDK 004.421:621.396.67 IFAC 5.8.3;2.8.3 Original scientific paper In this paper a new effective optimization algorithm called Genetical Swarm Optimization (GSO) is presented. This is an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). Preliminary analyses are here presented with respect to the other optimization techniques dealing with a classical optimization problem. The optimized design of a printed reflectarray antenna is finally reported with numerical results.
31 citations
TL;DR: In this paper, a new heuristic called adaptive genetic algorithm (AGA) was proposed for an efficient exploration of the search space, which was tested on a bi-objective permutation flow-shop scheduling problem, in order to evaluate the interest of each type of cooperation.
Abstract: This is a summary of the main results presented in the author’s PhD thesis. This thesis was supervised by El-Ghazali Talbi, and defended on 21 June 2005 at the University of Lille (France). It is written in French and is available at http://www.lifl.fr/~basseur/These.pdf. This work deals with the conception of cooperative methods in order to solve multi-objective combinatorial optimization problems. Many cooperation schemes between exact and/or heuristic methods have been proposed in the literature. We propose a classification of such schemes. We propose a new heuristic called adaptive genetic algorithm (AGA), that is designed for an efficient exploration of the search space. We consider several cooperation schemes between AGA and other methods (exact or heuristic). The performance of these schemes are tested on a bi-objective permutation flow-shop scheduling problem, in order to evaluate the interest of each type of cooperation.
25 citations
TL;DR: A new SLS algorithm for MAXSAT is proposed based on an unconventional distribution known as the Bose-Einstein distribution in quantum physics that provides a stochastic initialization scheme to an efficient and very simple heuristic inspired by the co-evolution process of natural species and called Extremal Optimization (EO).
Abstract: Stochastic local search algorithms (SLS) have been increasingly applied to approximate solutions of the weighted maximum satisfiability problem (MAXSAT), a model for solutions of major problems in AI and combinatorial optimization. While MAXSAT instances have generally a strong intrinsic dependency between their variables, most of SLS algorithms start the search process with a random initial solution where the value of each variable is generated independently with the same uniform distribution. In this paper, we propose a new SLS algorithm for MAXSAT based on an unconventional distribution known as the Bose-Einstein distribution in quantum physics. It provides a stochastic initialization scheme to an efficient and very simple heuristic inspired by the co-evolution process of natural species and called Extremal Optimization (EO). This heuristic was introduced for finding high quality solutions to hard optimization problems such as colouring and partitioning. We examine the effectiveness of the resulting algorithm by computational experiments on a large set of test instances and compare it with some of the most powerful existing algorithms. Our results are remarkable and show that this approach is appropriate for this class of problems.
TL;DR: Using a simple, annealed model, some of the key features of the recently introduced extremal optimization heuristic are demonstrated and it is shown that the dynamics of local search possesses a generic critical point under the variation of its sole parameter.
Abstract: Using a simple, annealed model, some of the key features of the recently introduced extremal optimization heuristic are demonstrated. In particular, it is shown that the dynamics of local search possesses a generic critical point under the variation of its sole parameter, separating phases of too greedy (non-ergodic, jammed) and too random (ergodic) exploration. Comparison of various local search methods within this model suggests that the existence of the critical point is essential for the optimal performance of the heuristic.
TL;DR: In this paper, a two-step approach is adopted wherein a simplified thermal model is developed to search for the optimum radiator/solar absorber areas, and then the results are implemented in a detailed thermal model to verify the temperature distribution, thereby reducing computational time.
Abstract: This paper presents a strategy for a quick determination of the optimum configuration for radiators and solar absorbers in a spacecraft thermal design, to minimize heater power consumption and maximize temperature margins. It is particularly useful when applied to multimission platforms in which the thermal design is adapted for different orbits and operational modes. A two-step approach is adopted wherein a simplified thermal model is developed to search for the optimum radiator/solar absorber areas, and then the results are implemented in a detailed thermal model to verify the temperature distribution, thereby reducing computational time, a common drawback in complex engineering optimization problems. If necessary, small adjustments are then made in the radiator/solarabsorberconfiguration.Thesearchfortheoptimumdesignisaccomplishedusingarecentlyproposed global search metaheuristic, called generalized extremal optimization. Based on a model of natural evolution, it is easy to implement and has only one free parameter to adjust, making no use of derivatives. This paper presents the strategy as applied to the thermal design of the Brazilian Multimission Platform now under development. Nomenclature Ai = area of the radiator or solar absorber of node i, m 2 a = weighting factor for heater power consumption bl = weighting factor for temperature deviation for critical case l
01 Oct 2006
TL;DR: A hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the particle swarm optimization (PSO) and genetic algorithms (GA).
Abstract: In this paper a new effective optimization algorithm called Genetical Swarm Optimization (GSO) is presented. This is an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). Preliminary analyses are here presented with respect to the other optimization techniques dealing with a classical optimization problem. The optimized design of a printed reflectarray antenna is finally reported with numerical results.
01 Jan 2006
01 Nov 2006
TL;DR: The ACO algorithm is proposed to optimize the solutions under multi-objective constraint and the improved performance of solutions from this algorithm will display in the experimental results.
Abstract: The flow shop problem with multiple objectives is always difficult to find optimal solutions, especially in large-scale problem. However the Ant Colony Optimization algorithms (ACO) is an effective method for solving hard combinatorial optimization problems. Therefore, we want to use the ACO algorithm to search optimal schedules of two machines flow shop problem under multi-objective constraint. Then our ACO algorithm is proposed to optimize the solutions under multi-objective constraint and the improved performance of solutions from our ACO algorithm will display in our experimental results.
16 Oct 2006
TL;DR: A quickly convergent method of the ACO algorithm with evolutionary operator (ACOEO) with crossover and mutation operator together provide a search capability that enhance rate of convergence and clearly shows that ACOEO has the property of effectively guiding the search towards promising regions in the search space.
Abstract: Ant colony optimization (ACO) is an optimization computation inspired by the study of the ant colonies? behavior. The combinational optimization process sometimes is based on the pheromone model and solution construction process. It remains a computational bottleneck because the ACO algorithm costs too much time to find an optimal solution for large-scale optimization problems. In this paper, a quickly convergent method of the ACO algorithm with evolutionary operator (ACOEO) is presented. In the method, crossover and mutation operator together provide a search capability that enhance rate of convergence. In addition, we adopt a dynamic selection means based on the fitness of each ant. The tours of better ants have high opportunity to obtain pheromone updating. Finally, our research clearly shows that ACOEO has the property of effectively guiding the search towards promising regions in the search space. The computer simulations demonstrate that the convergence speed and optimization performance are better than the ACO algorithm.
11 Sep 2006
TL;DR: A grid-based ant colony algorithm for automatic 3D hose routing that uses the tessellated format of the obstacles and the generated hoses in order to detect collisions.
Abstract: Ant colony algorithms applied to difficult combinatorial optimization problems such as the traveling salesman problem (TSP) and the quadratic assignment problem. In this paper we propose a grid-based ant colony algorithm for automatic 3D hose routing. Algorithm uses the tessellated format of the obstacles and the generated hoses in order to detect collisions. The representation of obstacles and hoses in the tessellated format greatly helps the algorithm towards handling free-form objects and speed up the computations. The performance of the algorithm has been tested on a number of 3D models.
22 Jun 2006
TL;DR: Examples with continuous and discrete parameters also known solutions and varying degrees of complexities are presented as an illustration for solving a large class of process optimization problems in petroleum engineering.
Abstract: The objective of the research presented in this paper is to investigate the application of a metaheuristic algorithm called Ant Colony Algorithmto petroleum engineering problems. This algorithm usually used for discrete domains, but with some modifications could be applied to continuous optimization. In this Paper, two examples with continuous and discrete parameters also known solutions and varying degrees of complexities are presented as an illustration for solving a large class of process optimization problems in petroleum engineering. Results of case studies show ability of Ant Colony Algorithm to provide fast and accurate solutions.
15 May 2006
TL;DR: A new effective optimization algorithm called genetical swarm optimization (GSO) will be presented, developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures.
Abstract: In this paper a new effective optimization algorithm called genetical swarm optimization (GSO) will be presented. It has been developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the particle swarm optimization (PSO) and genetic algorithms (GA). This algorithm is essentially a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). The algorithm is tested here with respect to the other optimization techniques dealing with the optimal design of an elliptical reflectarray antenna with printed elements and an off-set feed.
TL;DR: The proposed hybrid approach for the recursive bisection of finite elements meshes is examined by decomposing two mesh examples and comparing them with a well known finite elements domain decomposer.
Journal Article•
TL;DR: To improve the algorithm, the convergence of this algorithm applied to TSP is analyzed in detail and some conclusions about the improvement of the speed of convergence are obtained.
Abstract: The convergence problems of ant colony optimization algorithm is studiedTo improve the algorithm,the convergence of this algorithm applied to TSP is analyzed in detailThe algorithm will be certain to converge to the optimal solution under the condition 0q_01In addition,the influence on its convergence caused by the properties of the closed path,heuristic functions,the pheromone and q_0 is analyzedBased on it,some conclusions about the improvement of the speed of convergence are obtained
14 Mar 2006
TL;DR: This paper discusses how to evolve the ant algorithm by reducing the number of control parameters and improve their performance, which result in speeding up ant algorithm compared to classical one.
Abstract: Ant Algorithm is a metaheuristic used to solve combinatorial optimization problems. As with other metaheuristic, like evolutionary methods, Ant Algorithms often show good optimization behavior but are slow when compared to classical heuristics. This problem happened due to the large number of control parameters used. Those parameters, that produce best performance, may be selected hand -tuned or using a systematic procedure. In this paper, we discuss how to evolve the ant algorithm by reducing the number of those parameters and improve their performance. This modification result in speeding up ant algorithm compared to classical one. A simple implementation of this approach tested on the traveling salesman problem (TSP) and many other problems. The results show that the modified ant algorithms have good performance compared to the original Ant Algorithm.
TL;DR: The article is devoted to the electrical network optimization by genetic algorithms—the optimization method based on the simulation of the biological evolution, a many-dimensional nonlinear discrete optimization problem, which requires the usage of heuristic or combinatorial algorithms.
Abstract: The article is devoted to the electrical network optimization by genetic algorithms—the optimization method based on the simulation of the biological evolution. This problem is a many-dimensional nonlinear discrete optimization problem, which requires the usage of heuristic or combinatorial algorithms. The mathematical model and algorithm of the given problem are developed. The presented methodology is applied for the IEEE 30-bus test system. Copyright © 2006 John Wiley & Sons, Ltd.
TL;DR: This paper presents two methods of extremal optimization which are motivated by the immune system and the ant’s foraging methods and shows how these methods can be applied to multiobjective combinatorial problems.
03 Nov 2006
TL;DR: It is concluded that HACS is an effective and efficient way to solve combinatorial optimization problems.
Abstract: The Ant Colony System (ACS) algorithm is vital in solving combinatorial optimization problems. However, the weaknesses of premature convergence and low efficiency greatly restrict its application. In order to improve the performance of the algorithm, the Hybrid Ant Colony System (HACS) is presented by introducing the pheromone adjusting approach, combining ACS with saving and interchange methods, etc. Furthermore, the HACS is applied to solve the Vehicle Routing Problem with Time Window (VRPTW). By comparing the computational results with the previous findings, it is concluded that HACS is an effective and efficient way to solve combinatorial optimization problems.
11 Sep 2006
TL;DR: An immune-inspired algorithm specially designed to deal with combinatorial optimization is applied here to solve time-varying TSP instances, with the cost of going from one city to the other being a function of time.
Abstract: Multimodal optimization algorithms are being adapted to deal with dynamic optimization, mainly due to their ability to provide a faster reaction to unexpected changes in the optimization surface. The faster reaction may be associated with the existence of two important attributes in population-based algorithms devoted to multimodal optimization: simultaneous maintenance of multiple local optima in the population; and self-regulation of the population size along the search. The optimization surface may be subject to variations motivated by one of two main reasons: modification of the objectives to be fulfilled and change in parameters of the problem. An immune-inspired algorithm specially designed to deal with combinatorial optimization is applied here to solve time-varying TSP instances, with the cost of going from one city to the other being a function of time. The proposal presents favorable results when compared to the results produced by a high-performance ant colony optimization algorithm of the literature.
10 Jul 2006
TL;DR: The proposed Gradient-based Continuous Ant Colony Optimization (GCACO) method is applied to several benchmark problems and the stigmergic communication is simulated through considering certain direction vectors which are memorized.
Abstract: A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vectors which are memorized. These vectors are normalized gradient vectors that are calculated using the values of the evaluation function and the corresponding values of object variables. The proposed Gradient-based Continuous Ant Colony Optimization (GCACO) method is applied to several benchmark problems * # , ./% ** # 0 $ 1 , ./% *** # D ow nl oa de d fr om jc m e. iu t.a c. ir at 9 :1 7 IR S T o n W ed ne sd ay O ct ob er 2 1s t 2 02 0
01 Aug 2006
TL;DR: It is testified by the experiment that the novel algorithm is better than before and accelerates the convergence speed.
Abstract: Time ant colony algorithm has good effect on combinatorial optimization problems as well as that of the ant colony algorithm while it has the shortcoming of long convergence time. A new method combined with genetic algorithms is proposed. Firstly a genetic algorithms procedure is used to solve the problem in specifying time. Secondly the solution having gotten is used to distribute the original pheromone. At the last the time ant colony algorithm is used to search the optimal solution, which supposed that each ant's velocity is the same and all ants are crawling in full time. The new method accelerates the convergence speed. It is testified by the experiment that the novel algorithm is better than before.