The aim of this paper is to propose a new particle swarm optimization algorithm to solve a hybrid flowshop scheduling with sequence-dependent setup times problem, which is of great importance in the industrial context. This algorithm is called diversified particle swarm optimization algorithm which is a generalization of particle swarm optimization algorithm and inspired by an anarchic society ...

In order to overcome the defects of the slow convergence rate of the traditional Genetic Algorithm and basic Particle Swarm Optimization drops into local optimum easily, an improved Particle Swarm Optimization algorithm based on hybrid algorithm is proposed and applied to the signal detection for MIMO-OFDM system. The algorithm optimizes the basic Particle Swarm Optimization algorithm and some ...

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow ap...

This paper presents a short study on the hybridization of a swarm based optimization algorithm with a single agent based algorithm. Swarm based algorithms and single agent based algorithms have each distinct advantages and disadvantages. One goal of the presented work is to combine the concepts of the two different algorithms such that a more effective optimization routine results. In particula...

The parts optimization are very important for scroll compressor design. According to existing problems of current optimization algorithm and actual optimization problems, the improved optimization algorithm—genetic-particle swarm optimization (GA-PSO) is proposed for scroll plate optimization. The optimization method integrates crossover of genetic algorithm (GA) and evolutionary mechanism of p...

Standard particle swarm optimization is easy to fall into local optimum and has the problem of low precision. To solve these problems, the paper proposes an effective approach, called particle swarm optimization based on multiple swarms and opposition-based learning, which divides swarm into two subswarms. The 1st sub-swarm employs PSO evolution model in order to hold the self-learning ability;...

in this paper, we propose a novel algorithm to enhance the noisy speech in the framework of dual-channel speech enhancement. the new method is a hybrid optimization algorithm, which employs the  combination of  the  conventional θ-pso and the shuffled sub-swarms particle optimization (sspso) technique. it is known that the θ-pso algorithm has better optimization performance than standard pso al...

We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically. The proposed algorithm is applied to different unconstrained, constrained, minimax and integer programming problems and the obtained results are c...

This article presents a hybrid evolutionary algorithm (PSABC) based on Artificial Bee Colony (ABC) and particle swarm optimization (PSO). Both of the algorithms are co-operative, population-based global search swarm intelligence met heuristics. The core of this algorithm is using PSO to optimize the fitness value of population in ABC. For Evaluation purpose, the proposed algorithm is tested on ...