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: This paper presents the ACO-based algorithm that is used to find the global minimum of a nonconvex function, based on that each ant searches only around the best solution of the previous iteration.
Abstract: The ant colony optimization (ACO) algorithms, which are inspired by the behaviour of ants to find solutions to combinatorial optimization problem, are multi-agent systems. This paper presents the ACO-based algorithm that is used to find the global minimum of a nonconvex function. The algorithm is based on that each ant searches only around the best solution of the previous iteration. This algorithm was tested on some standard test functions, and successful results were obtained. Its performance was compared with the other algorithms, and was observed to be better.
25 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
Posted Content•
TL;DR: A framework for model reduction and heuristic optimization is applied to two representative models, indicating its applicability to a wide range of agent-based models and a statistical measure for model similarity is proposed.
Abstract: Questions concerning how one can influence an agent-based model in order to best achieve some specific goal are optimization problems. In many models, the number of possible control inputs is too large to be enumerated by computers; hence methods must be developed in order to find solutions that do not require a search of the entire solution space. Model reduction techniques are introduced and a statistical measure for model similarity is proposed. Heuristic methods can be effective in solving multi-objective optimization problems. A framework for model reduction and heuristic optimization is applied to two representative models, indicating its applicability to a wide range of agent-based models. Results from data analysis, model reduction, and algorithm performance are assessed.
25 citations
Journal Article•
TL;DR: Simulation study and performance comparison show that the improved algorithm, with high efficiency and robustness, appears self -adaptive and can converge at the global optimum with a high probability.
Abstract: In order to solve the premature convergence problem of the basic Ant Colony Optimization algorithm, a promising modification based on the information entropy is proposed. The main idea is to evaluate stability of the current space of represented solutions using information entropy, which is then applied to turning of the algorithm's parameters. The path selection and evolutional strategy are controlled by the information entropy self-adaptively. Simulation study and performance comparison with other Ant Colony Optimization algorithms and other meta-heuristics on Traveling Salesman Problem show that the improved algorithm, with high efficiency and robustness, appears self -adaptive and can converge at the global optimum with a high probability. The work proposes a more general approach to evolutionary-adaptive algorithms related to the population's entropy and has significance in theory and practice for solving the combinatorial optimization problems.
25 citations
10 Jun 2009
TL;DR: A novel ACO algorithm is introduced for the automated design of optimal control policies for continuous-state dynamic systems that integrates the multi-agent optimization heuristic of ACO with a fuzzy partitioning of the state space of the system.
Abstract: Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for Combinatorial Optimization Problems. While being very successful for various NPcomplete optimization problems, ACO is not trivially applicable to control problems. In this paper a novel ACO algorithm is introduced for the automated design of optimal control policies for continuous-state dynamic systems. The so called Fuzzy ACO algorithm integrates the multi-agent optimization heuristic of ACO with a fuzzy partitioning of the state space of the system. A simulated control problem is presented to demonstrate the functioning of the proposed algorithm.
25 citations