Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

A Hybrid Particle Swarm Optimization – Simulated Annealing Algorithm for the Probabilistic Travelling Salesman Problem

Abstract: The Probabilistic Traveling Salesman Problem (PTSP) is a variation of the well known Traveling Salesman Problem (TSP). This problem arises when the information about customers demand is not available at the moment of the tour generation and/or the tour re-calculating cost is too elevated. In this article, a Hybrid Algorithm combining Particle Swarm Optimization (PSO) and Simulated Annealing (SA) is proposed, in order to solve the PTSP. The PSO heuristic offers a simple structured algorithm which supplies a high level of exploration and fast convergence, compared with other evolutionary algorithms. The SA algorithm is used to improve the particle diversity and to avoid the algorithm being trapped into local optimum. Two well-known benchmarks of the literature are used and the proposed PSO-SA algorithm obtains acceptable results. In fact, the hybrid algorithm improves the performance of simple PSO algorithm for all instances.

Genetic algorithm with search area adaptation for the function optimization and its experimental analysis

Abstract: The paper applies a method, Genetic algorithm with Search area Adaptation (GSA), to function optimization. In a previous study (H. Someya and M. Yamamura, 1999), GSA was proposed for the floorplan design problem and it showed better performance than several existing methods. We believe that investigation of the searching behavior of the algorithm is important. However, since the floorplan design problem is a combinatorial optimization problem, we do not know in detail why GSA works well. Thus, we apply GSA to function optimization in order to study the searching behavior in detail. In the function optimization, several benchmarks have been proposed, and their optima and landscapes are known. There is another reason to apply GSA to function optimization: we would like to propose a superior method for function optimization. Through several experiments, we have confirmed that GSA works adaptively and it shows higher performance than existing methods.

Fuzzy goal programming-based ant colony optimization algorithm for multi-objective topology design of distributed local area networks

Abstract: Topology design of a distributed local area network (DLAN) is a complex optimization problem and has been generally modelled as a single-objective optimization problem. Traditionally, iterative techniques such as genetic algorithms and simulated annealing have been used to solve the problem. In this paper, we formulated the DLAN topology design problem as a multi-objective optimization problem considering five design objectives. These objectives are network reliability, network availability, average link utilization, monetary cost, and average network delay. The multi-objective nature of the problem has been addressed by incorporating a fuzzy goal programming approach to combine the individual design objectives into a single-objective function. The objective function is then optimized using the ant colony algorithm adapted for the problem. The performance of the proposed fuzzy goal programming-based ant colony optimization algorithm (GPACO) is evaluated with respect to the algorithm control parameters, namely pheromone deposit and evaporation rate, colony size and heuristic values. A comparative study was also done using four other multi-objective optimization algorithms which are non-dominated sorting genetic algorithm II, archived multi-objective simulated annealing algorithm, lexicographic ant colony optimization, and Pareto-dominance ant colony optimization. Results revealed that, in general, GPACO was able to find solutions of higher quality as compared to the other four algorithms.

Ant Colony Optimization

Abstract: Ant colony optimization (ACO) is a new natural computation method from mimic the behaviors of ant colony. It is a very good combination optimization method. Ant colony optimization algorithm was recently proposed algorithm, it has strong robustness as well as good distributed calculative mechanism, and it is easy to combine with other methods, and the well performance has been shown on resolving the complex optimization problem. An ant colony optimization approach for the resource-constrained project scheduling problem (RCPSP) is presented. The TSP problem is chosen as example for introducing the basic principle of ACO, and several improvement algorithms are present for the problem of ACO.

Ant colony optimization for water distribution network design: a comparative study

Abstract: The optimal design of looped water distribution networks is a major environmental and economic problem with applications in urban, industrial and irrigation water supply. Traditionally, this complex problem has been solved by applying single-objective constrained formulations, where the goal is to minimize the network investment cost subject to pressure constraints. In order to solve this highly complex optimization problem some authors have therefore proposed using heuristic techniques for their solution. Ant Colony Optimization (ACO) is a metaheuristic that uses strategies inspired by real ants to solve optimization problems. This paper presents and evaluates the performance of a new ACO implementation specially designed to solve this problem, which results in two benchmark networks outperform those obtained by genetic algorithms and scatter search.