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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24 Jul 2016
TL;DR: A multi-phase strategy for dynamic resource allocation is proposed for some special optimization problems where the evolutionary process cannot be explicitly divided into two phases, under the decomposition-based multiobjective evolutionary optimization framework.
Abstract: In this paper, a multi-phase strategy for dynamic resource allocation is proposed for some special optimization problems where the evolutionary process cannot be explicitly divided into two phases, under the decomposition-based multiobjective evolutionary optimization framework. Based on the evolutionary status, a switching mechanism is adopted to adaptively use either convergence or diversity information in the external archive, to guide the evolutionary search in the working population. The proposed algorithm is compared with six well-known multiobjective evolutionary algorithms on multiobjective travelling salesman problem (MOTSP). Experimental results show that our proposed algorithm performs better than other compared algorithms.
3 citations
TL;DR: This paper presents a modified form of the basic ACO algorithm, obtained by introducing a new operation known as restoring operation for solving the SCTSP problem, where the low-quality path/solution given by any ant is replaced by a nearest better solution.
Abstract: This paper presents an advanced ant colony optimisation approach to solve sub-tour constant travelling salesman problem (SCTSP) with time limit in fuzzy environment. In SCTSP, there is a set of sub-tours (two cities for each sub-tour). A traveller must complete his/her tour with predetermined sequence of distinct sub-tours. In this proposed model, the total time of travel must not exceed a predetermined time limit, i.e., the salesman should maintain a maximum time limit to complete his/her tour. In this paper, we present a modified form of the basic ACO algorithm, obtained by introducing a new operation known as restoring operation for solving the problem. In restoring operation, the low-quality path/solution given by any ant is replaced by a nearest better solution. The proposed problem is solved by considering fuzzy travel costs and time. Fuzzy credibility measure and graded mean integration method are used to obtain optimal decision. Computational results with different datasets are presented for illustration.
3 citations
TL;DR: In this paper, a general bottleneck combinatorial optimization problem with uncertain element weights modeled by fuzzy intervals is considered, and some algorithms for finding a solution according to the introduced concepts and evaluating optimality of solutions and elements are provided.
3 citations
01 Dec 2009
TL;DR: A brief review of the development process of Ant Algorithms, some of the nowadays best performing ant colony optimization variants are presented, and some theoretic and realistic research points for the ant algorithm are proposed.
Abstract: Ant algorithm optimization is a technique for optimization that was introduced in the early 1990's. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. It has widely use in discrete optimization problems, to continuous optimization problems. In this article, we give a brief review of the development process of Ant Algorithms, present some of the nowadays best performing ant colony optimization variants. By systematic review the theory of mainstream ant algorithm, we summarizing some important theoretical results. Finally, we propose some theoretic and realistic research points for the ant algorithm.
3 citations
3 citations