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.
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TL;DR: Performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building (nZEB) where more than 1.610 solutions would be possible is compared.
218 citations
TL;DR: The proposed competitive and cooperative co-evolutionary multi-objective particle swarm optimization algorithm (CCPSO) is validated through comparisons with existing state-of-the-art multi- objective algorithms using established benchmarks and metrics and demonstrated that CCPSO shows competitive, if not better, performance as compared to the other algorithms.
217 citations
TL;DR: This paper considers the multi-objective reliability redundancy allocation problem of a series system where the reliability of the system and the corresponding designing cost are considered as two different objectives and a fuzzy multi- objective optimization problem (FMOOP) is formulated from the original crisp optimization problem.
216 citations
TL;DR: The evolution of each individual of the total population, which consists of the parents and the offspring, is realized with the use of a Particle Swarm Optimizer where each has to improve its physical movement following the basic principles of Particles Swarm Optimization until it will obtain the requirements to be selected as a parent.
Abstract: Usually in a genetic algorithm, individual solutions do not evolve during their lifetimes: they are created, evaluated, they may be selected as parents to new solutions and they are destroyed. However, research into memetic algorithms and genetic local search has shown that performance may be improved if solutions are allowed to evolve during their own lifetimes. We propose that this solution improvement phase can be assisted by knowledge stored within the parent solutions, effectively allowing parents to teach their offspring how to improve their fitness. In this paper, the evolution of each individual of the total population, which consists of the parents and the offspring, is realized with the use of a Particle Swarm Optimizer where each of them has to improve its physical movement following the basic principles of Particle Swarm Optimization until it will obtain the requirements to be selected as a parent. Thus, the knowledge of each of the parents, especially of a very fit parent, has the possibility to be transferred to its offspring and to the offspring of the whole population, and by this way the proposed algorithm has the possibility to explore more effectively the solution space. These ideas are applied in a classic combinatorial optimization problem, the vehicle routing problem, with very good results when applied to two classic benchmark sets of instances.
216 citations
19 Jun 2004
TL;DR: A heuristic rule, the smallest position value (SPV) rule, is developed to enable the continuous particle swarm optimization algorithm to be applied to all classes of sequencing problems, which are NP-hard in the literature.
Abstract: In This work we present a particle swarm optimization algorithm to solve the single machine total weighted tardiness problem. A heuristic rule, the smallest position value (SPV) rule, is developed to enable the continuous particle swarm optimization algorithm to be applied to all classes of sequencing problems, which are NP-hard in the literature. A simple but very efficient local search method is embedded in the particle swarm optimization algorithm. The computational results show that the particle swarm algorithm is able to find the optimal and best-known solutions on all instances of widely used benchmarks from the OR library.
216 citations