Using memory and fuzzy rules in a co-operative multi-thread strategy for optimization

Pelta, David A.; Sancho-Royo, Alejandro; Cruz, Carlos; Verdegay, José L.

doi:10.1016/j.ins.2005.06.007

Cited by 49 publications

(21 citation statements)

References 23 publications

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“…For the first case, we based on a concept previously applied in the context of multi-thread cooperative strategy for optimization [29]. The basic idea is to define the fuzzy set of "bad swarms" and then, when a swarm needs to be evaluated, we will measure its degree of membership to that set.…”

Section: Control Rule For Swarmsmentioning

confidence: 99%

Efficient multi-swarm PSO algorithms for dynamic environments

2011

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Particle swarm optimization has been successfully applied in many research and application areas because of its effectiveness and easy implementation. In this work we extend one of its variants to address multi-modal dynamic optimization problems, the multi-swarm PSO (mPSO) proposed by Blackwell and Branke. The aim of our proposal is to increase the efficiency of this algorithm. To this end, we propose techniques operating at swarm level: one of which divides each swarm into two groups depending on the quality of the particles for facing the loss of diversity, and the other control the number of active swarms during the run using a fuzzy rule. A detailed experimental analysis shows the robustness of our proposal.

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Section: Control Rule For Swarmsmentioning

confidence: 99%

Efficient multi-swarm PSO algorithms for dynamic environments

2011

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“…The third and main method is an adaptation of a centralised multi-threaded cooperative strategy based on trajectory heuristics that have been previously applied on the scope of static optimization problems [7,13,14]. The structure and working of this strategy will be explained first, followed by a description of the steps followed to adapt it to the resolution of DOPs.…”

Section: Multi-threaded Cooperative Strategymentioning

confidence: 99%

“…In previous works [7,13,14] we have studied how the use of cooperation can improve the results on static optimization problems. The method developed in those works, called CooperativeStrategy, consists on a centralized cooperation scheme where a coordinator manages the information exchange among the trajectory-based solvers.…”

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confidence: 99%

A cooperative strategy for solving dynamic optimization problems

2010

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Optimization in dynamic environments is a very active and important area which tackles problems that change with time (as most real-world problems do). In this paper we present a new centralized cooperative strategy based on trajectory methods (tabu search) for solving Dynamic Optimization Problems (DOPs). Two additional methods are included for comparison purposes. The first method is a Particle Swarm Optimization variant with multiple swarms and different types of particles where there exists an implicit cooperation within each swarm and competition among different swarms. The second method is an explicit decentralized cooperation scheme where multiple agents cooperate to improve a grid of solutions. The main goals are: firstly, to assess the possibilities of trajectory methods in the context of DOPs, where populational methods have traditionally been the recommended option; and secondly, to draw attention on explicitly including cooperation schemes in methods for DOPs. The results show how the proposed strategy can consistently outperform the results of the two other methods.

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“…These ideas have been applied in several fields both explicitly and implicitly. For example, we should mention the multi-thread parallel metaheuristics (Bouthillier and Crainic 2005;Cruz and Pelta 2009;Pelta et al 2006), where several heuristics that are executed in parallel, cooperate sharing performance information in order to reach promising regions of the search space, avoid local minima, determine the most suitable heuristic, etc. Also multi-agents systems, where a set of very simple entities ('ants' in ACO, Dorigo and Stützle (2004) or 'particles' in PSO, Kennedy and Eberhart (2001)) proceed in a cooperative way to improve progressively a population of solutions.…”

Section: Self-adaptation and Cooperative Strategiesmentioning

confidence: 99%

Solving multiple instances at once: the role of search and adaptation

2010

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Having in mind the idea that the computational effort and knowledge gained while solving a problem's instance should be used to solve other ones, we present a new strategy that allows to take advantage of both aspects. The strategy is based on a set of operators and a basic learning process that is fed up with the information obtained while solving several instances. The output of the learning process is an adjustment of the operators. The instances can be managed sequentially or simultaneously by the strategy, thus varying the information available for the learning process. The method has been tested on different SAT instance classes and the results confirm that (a) the usefulness of the learning process and (b) that embedding problem specific algorithms into our strategy, instances can be solved faster than applying these algorithms instance by instance.

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Using memory and fuzzy rules in a co-operative multi-thread strategy for optimization

Cited by 49 publications

References 23 publications

Efficient multi-swarm PSO algorithms for dynamic environments

Efficient multi-swarm PSO algorithms for dynamic environments

A cooperative strategy for solving dynamic optimization problems

Solving multiple instances at once: the role of search and adaptation

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