Topic
Multi-swarm optimization
About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: Empirical studies demonstrate that the proposed surrogate-assisted cooperative swarm optimization algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.
Abstract: Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.
267 citations
••
TL;DR: This paper designs a modified Multi-Swarm PSO (MSPSO) to solve discrete problems, and proposes an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM) with F-score method to achieve higher generalization capability.
267 citations
••
TL;DR: An efficient optimization algorithm called teaching–learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems and the results show the better performance of the proposed algorithm.
Abstract: An efficient optimization algorithm called teaching–learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, ϵ-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.
267 citations
••
TL;DR: The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal solutions, but also to obtain good distributions in both the decision and objective spaces.
Abstract: This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective function value The proposed method features an index-based ring topology to induce stable niches that allow the identification of a larger number of Pareto-optimal solutions, and adopts a special crowding distance concept as a density metric in the decision and objective spaces The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal solutions, but also to obtain good distributions in both the decision and objective spaces In addition, new multimodal multiobjective optimization test functions and a novel performance indicator are designed for the purpose of assessing the performance of the proposed algorithms An effectiveness validation study is carried out comparing the proposed method with five other algorithms using the benchmark functions to prove its effectiveness
267 citations
••
TL;DR: This hybrid algorithm, called maximum relevance minimum redundancy PSO (mr^2PSO), is novel in the sense that it uses the mutual information available from the filter model to weigh the bit selection probabilities in the discrete PSO.
267 citations