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Extremal optimization

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


Papers
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Proceedings ArticleDOI
01 Aug 2014
TL;DR: The use of Multi-Objective concept to solve the Traveling Salesman Problem (TSP) and a comparative study with other algorithms existing in the literature has shown a better performance of the algorithm (pMOPSO).
Abstract: This paper describe the use of Multi-Objective concept to solve the Traveling Salesman Problem (TSP). The traveling salesman problem is defined as an NP-hard problem. The resolution of this kind of problem is based firstly on exact methods and after that is based on single objective based methods as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Firstly, a short description of the Multi-objective Particles swarm optimization (MOPSO) is given as an efficient technique to use for many real problems. Based on the concept of Pareto dominance, the process of implementation of the algorithm consists of two stages. First, when executing a multi-objective Particle Swarm Optimization (MOPSO), a ranking operator is applied to the population in a predefined iteration to build an initial archive using e-dominance The TSP problem is characterized by two contradictory objectives as minimize the total distance traveled by a particle and minimize the total time. An experimental study is conducted in this paper. A comparative study with other algorithms existing in the literature has shown a better performance of our algorithm (pMOPSO).

7 citations

Journal ArticleDOI
TL;DR: In this article, the performance of extremal optimization (EO), flat-histogram and equal-hit algorithms for finding spin-glass ground states is compared, and the first-passage times to a ground state are computed.
Abstract: We compare the performance of extremal optimization (EO), flat-histogram and equal-hit algorithms for finding spin–glass ground states. The first-passage-times to a ground state are computed. At optimal parameter of τ=1.15, EO outperforms other methods for small system sizes, but equal-hit algorithm is competitive to EO, particularly for large systems. Flat-histogram and equal-hit algorithms offer additional advantage that they can be used for equilibrium thermodynamic calculations. We also propose a method to turn EO into a useful algorithm for equilibrium calculations.

7 citations

Proceedings ArticleDOI
06 Jul 2014
TL;DR: Two hybrid immigrants, i.e., non-interactive and interactive, schemes are proposed to combine the merits of the aforementioned immigrants schemes and showed that the hybridization of immigrants further improves the performance of ACO algorithms.
Abstract: Dynamic optimization problems (DOPs) have been a major challenge for ant colony optimization (ACO) algorithms. The integration of ACO algorithms with immigrants schemes showed promising results on different DOPs. Each type of immigrants scheme aims to address a DOP with specific characteristics. For example, random and elitism-based immigrants perform well on severely and slightly changing environments, respectively. In this paper, two hybrid immigrants, i.e., non-interactive and interactive, schemes are proposed to combine the merits of the aforementioned immigrants schemes. The experiments on a series of dynamic travelling salesman problems showed that the hybridization of immigrants further improves the performance of ACO algorithms.

7 citations

Proceedings ArticleDOI
06 Oct 2002
TL;DR: The proposed method is applied to solve many TSPs (Traveling Salesman Problems), and it is found that the method is very effective and useful.
Abstract: Simulated annealing (SA) is an effective general heuristic method for solving many combinatorial optimization problems. This paper deals with the two problems in SA. One is the long computational time of the numerical annealings, and the solution to it is the parallel processing of SA. The other one is the determination of the appropriate temperature schedule in SA, and the solution to it is the introduction of an adaptive mechanism for changing the temperature. The multiple SA processes are performed in multiple processors, and the temperatures in the SA processes are determined by a genetic algorithms. The proposed method is applied to solve many TSPs (Traveling Salesman Problems), and it is found that the method is very effective and useful.

7 citations

Journal Article
TL;DR: This paper further extends the idea of this new biological optimization strategy to some other hard combinatorial optimization problems, including the multi attribute situation which lack of efficient solving methods.
Abstract: Ant algorithm is a newly emerged stochastic searching optimization algorithm in recent years It has been paid much attention to since the successful application in the famous travelling salesman problem This paper further extends the idea of this new biological optimization strategy to some other hard combinatorial optimization problems, including the multi attribute situation which lack of efficient solving methods The ability of optimization for the algorithm is tested experimentally which give encouraging results

7 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20232
202213
20217
20209
201922
201815