Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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TL;DR: Simulations show that the hybrid algorithm has remarkable global convergence ability, and can avoid the premature convergence effectively.
Abstract: A new hybrid algorithm based on differential evolution(DE) and extremal optimization(EO) was proposed to solve the premature convergence and low precision of standard differential evolution when it is applied to complex optimization problems.The key points of it lie in:the hybrid algorithm introduces the population-based extremal optimization algorithm in the iteration process of DE when population aggregation gets the high degree,which uses the volatility of EO to increase the diversity of population and the ability of breaking away from the local optimum.Simulations show that the hybrid algorithm has remarkable global convergence ability,and can avoid the premature convergence effectively.
2 citations
08 Apr 2015
TL;DR: The proposed load balancing algorithm is evaluated by experiments with simulated parallelized load balancing of distributed program graphs and aims at better convergence of the algorithm and better quality of program execution in terms of the execution time.
Abstract: The paper concerns parallel methods for Extremal Optimization (EO) applied for processor load balancing for distributed programs. In these methods the EO approach is used which is parallelized and extended by a guided search of next solution state. EO detects the best strategy of tasks migration leading to a reduction in program execution time. We assume a parallel improvement of the EO algorithm with guided state changes which provides a parallel search for a solution based on two step stochastic selection during the solution improvement based on two fitness functions. The load balancing improvements based on EO aim at better convergence of the algorithm and better quality of program execution in terms of the execution time. The proposed load balancing algorithm is evaluated by experiments with simulated parallelized load balancing of distributed program graphs.
2 citations
07 Apr 2014
TL;DR: Investigations have shown that the ACO algorithm seems to be very effective for solving the considered problem and can be recommended for solving other transportation problems.
Abstract: The paper concerns the introduced and defined problem which was called the Provider. This problem coming from practice and can be treated as a modified version of Travelling Salesman Problem. For solving the problem an algorithm called ACO based on ant colony optimization ideas has been created. The properties of the algorithm were tested using the designed and implemented experimentation system. The effectiveness of the algorithm was evaluated and compared to reference results given by another implemented Random Optimization algorithm called RO on the basis of simulation experiments. The reported investigations have shown that the ACO algorithm seems to be very effective for solving the considered problem. Moreover, the ACO algorithm can be recommended for solving other transportation problems.
2 citations
04 Dec 2006
TL;DR: This paper analyzes the queen ant strategy ASqueen more in detail by applying it to six kinds of city configurations included in the TSPLIB and proposes a new method named "stimulative queens ant strategy ASS queen", which shows better performance than the conventional Asqueen.
Abstract: Ant Colony Optimization (ACO) methods, which imitate a mechanism of pheromone secretion when ants carry food to their nest, are one of efficient heuristic search methods for combinational optimization problems such as traveling salesman problems (TSPs) and so on. In this paper, we analyze the Queen Ant Strategy AS_queen that is one of ACO methods more in detail by applying it to six kinds of city configurations included in the TSPLIB. Furthermore, in order to improve searching ability of the AS_queen, we propose a new method named "Stimulative Queen Ant Strategy AS_queen". As experimental results, we have clarified that the AS_queen shows better performance than the conventional AS_queen in the viewpoint of both "discovery rate of optimal solution" and "average number of iterations".
2 citations
15 Dec 2014
TL;DR: A treatment list of components is proposed, aiming to avoid the test of precedence constraints for every component and for every ant, in an Ant Colony Optimization algorithm for the Tasks to Workstations Assignment problem.
Abstract: When Ant System algorithm is used to solve combinatorial optimization problems with precedence constraints, the computation of components list that can be used to extend the current partial solution is time consuming. For every component, the algorithm tests if the precedence constraints are met. In this work, a treatment list of components is proposed, aiming to avoid the test of precedence constraints for every component and for every ant. The list is not a fixed sequence of components, but it is equivalent to a total order between predefined groups of components. Search biases can appear and some solutions are proposed. The use of this technique is exemplified by an Ant Colony Optimization algorithm for the Tasks to Workstations Assignment(TWA) problem. The effectiveness of the proposed approach is validated by computational tests.
2 citations