Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO. Hybridization is a method of combining two (or...

In this chapter, Deep Memory with Particle Swarm Optimization (DMPSO) algorithm is presented, which is based on Particle Swarm Optimization initialized by the particles of Deep Memory Greedy Search (DMGS). The Particle Swarm Optimization (PSO) is a population based optimization technique, where the population is called a swarm. In PSO, each particle represents a possible solution to the optimiz...

In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance...

This paper presents an optimal design of poly-phase induction motor using Quadratic Interpolation based Particle Swarm Optimization (QI-PSO). The optimization algorithm considers the efficiency, starting torque and temperature rise as objective function (which are considered separately) and ten performance related items including harmonic current as constraints. The QI-PSO algorithm was impleme...

this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...

This paper presents a Particle Swarm Optimization with Improved Inertia Wight (PSO-IIW) for Combined Heat and Power Economic Dispatch (CHPED) problem. The proposed PSO-IIW technique, which is a population based global search and optimization technique, has been developed to solve the CHPED problem. The CHPED problem is formulated as an optimization problem which is solved by the PSO-IIW techniq...

Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm Optimization (APSO) is proposed to inc...

The author proposes an extension of particle swarm optimization (PSO) for solving interval-valued optimization problems and applies the extended PSO to evolutionary training of neural networks (NNs) with interval weights. In the proposed PSO, values in the genotypes are not real numbers but intervals. Experimental results show that interval-valued NNs trained by the proposed method could well a...

This paper proposes a novel population-based evolution algorithm named grouping-shuffling particle swarm optimization (GSPSO) by hybridizing particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems. In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evo...

The catfish particle swarm optimization (CatfishPSO) algorithm is a novel swarm intelligence optimization technique. This algorithm was inspired by the interactive behavior of sardines and catfish. The observed catfish effect is applied to improve the performance of particle swarm optimization (PSO). In this paper, we propose fuzzy CatfishPSO (F-CatfishPSO), which uses fuzzy to dynamically chan...