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: By implementing the representation of state in particle swarm optimization (PSO), a variant of PSO called multi-state particle Swarm optimization (MSPSO) algorithm is proposed and shows that the newly introduced approach manage to obtain comparable results, compared to other algorithms in consideration.
Abstract: The binary-based algorithms including the binary particle swarm optimization (BPSO) algorithm are proposed to solve discrete optimization problems. Many works have focused on the improvement of the binary-based algorithms. Yet, none of these works have been represented in states. In this paper, by implementing the representation of state in particle swarm optimization (PSO), a variant of PSO called multi-state particle swarm optimization (MSPSO) algorithm is proposed. The proposed algorithm works based on a simplified mechanism of transition between two states. The performance of MSPSO algorithm is emperically compared to BPSO and other two binary-based algorithms on six sets of selected benchmarks instances of traveling salesman problem (TSP). The experimental results showed that the newly introduced approach manage to obtain comparable results, compared to other algorithms in consideration.
6 citations
06 Jul 2014
TL;DR: In this article, the authors investigated the development of an algorithm to solve the set covering problem by employing chemical reaction optimization (CRO), a general-purpose metaheuristic, and tested on a wide range of benchmark instances of SCP.
Abstract: The set covering problem (SCP) is one of the representative combinatorial optimization problems, having many practical applications. This paper investigates the development of an algorithm to solve SCP by employing chemical reaction optimization (CRO), a general-purpose metaheuristic. It is tested on a wide range of benchmark instances of SCP. The simulation results indicate that this algorithm gives outstanding performance compared with other heuristics and metaheuristics in solving SCP.
6 citations
16 Apr 2013
TL;DR: The new scheme, Simple Probabilistic Population Based Optimization scheme (SPPBO), is used also to classify existing metaheuristics, e.g., the Population-based Ant Colony Optimization algorithm (PACO) and the Simplified Swarm Optimization algorithms (SSO), and shows the close relationship between PACO and SSO.
Abstract: A new scheme is proposed for the design of probabilistic population based optimization algorithms for solving combinatorial optimization problems. The new scheme, Simple Probabilistic Population Based Optimization scheme (SPPBO), is used also to classify existing metaheuristics, e.g., the Population-based Ant Colony Optimization algorithm (PACO) and the Simplified Swarm Optimization algorithm (SSO). The classification shows the close relationship between PACO and SSO. This fact has not been recognized in the literature so far. SPPBO is also used to identify new metaheuristics that come up naturally as variants and combinations of PACO and SSO. An experimental study is done to evaluate and compare the different algorithms when applied to the Traveling Salesperson Problem. The results show which parts of the algorithms are helpful for obtaining a good optimization behaviour. In addition to the original PACO and SSO algorithms also some of the new combinations perform very well.
6 citations
25 May 2015
TL;DR: This paper presents a non-parameter method to identify the peaks of the multi-modal optimization problems provided that the peaks are characterized by a smaller objective values than their neighbors and by a relatively large distance from points with smaller objective value.
Abstract: This paper presents a non-parameter method to identify the peaks of the multi-modal optimization problems provided that the peaks are characterized by a smaller objective values than their neighbors and by a relatively large distance from points with smaller objective value. Using the identified peaks as the seeds, we decompose the population into some subpopulations and dynamically allocate the computational effort to different subpopulations. We evaluate the proposed approach on the CEC2015 single objective multi-niche optimization problems. The promising experimental results show its efficacy.
6 citations
23 Dec 2010
TL;DR: Computational experiments indicate that the proposed hybrid meta-heuristic for combinatorial optimization problems with specific reference to the travelling salesman problem is very efficient.
Abstract: This paper describes a hybrid meta-heuristic for combinatorial optimization problems with specific reference to the travelling salesman problem (TSP). The method is a combination of genetic algorithms (GA) and greedy randomized adaptive search procedures (GRASP). A new adaptive fuzzy greedy search operator is developed for this hybrid method. Computational experiments using a wide range of standard benchmark problems indicate that the proposed hybrid meta-heuristic is very efficient.
6 citations