The paper presents a novel particle swarm optimizer (PSO), called gender-hierarchy particle swarm optimizer based on punishment (GH-PSO). In the proposed algorithm, the social part and recognition part of PSO both are modified in order to accelerate the convergence and improve the accuracy of the optimal solution. Especially, a novel recognition approach, called general recognition, is presente...

In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. Specifically, an ILS module is proposed to generate a more effective and efficient exemplar, which could offer a more promising search direction to the PSO-ILS particle. Comparison is made on the PSO-ILS with 6 well-established PSO variants on 10 benchmark functio...

Particle Swarm Optimization is currently employed in several optimization and search problems due its ease and ability to find solutions successfully. A variant of PSO, called as Improved PSO has been developed in this paper and is hybridized with the simulated annealing approach to achieve better solutions. The hybrid technique has been employed, inorder to improve the performance of improved ...

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. Being a stochastic algorithm, PSO and its randomness present formidable challenge for the theoretical analysis of it, and few of the existing PSO improvements have make an effort to eliminate the random coefficients in the PSO updating formula. This paper analyzes the importance of the randomness in the...

Chaotic catfish particle swarm optimization (C-CatfishPSO) is a novel optimization algorithm proposed in this paper. C-CatfishPSO introduces chaotic maps into catfish particle swarm optimization (CatfishPSO), which increase the search capability of CatfishPSO via the chaos approach. Simple CatfishPSO relies on the incorporation of catfish particles into particle swarm optimization (PSO). The in...

A Maglev system was modeled by the exact feedback linearization to achieve two same linear subsystems. The proportional-integral-differential controllers (PID) based on particle swarm optimization (PSO) algorithm with four different inertia weights were then used to regulate both linear subsystems. These different inertia weights were Fixed Inertia Weight (FIW), Linear Descend Inertia Weight (L...

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the alg...

This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. In the new algorithm, each particle is attracted towards the best previous positions visited by its neighbors, in addition to the other aspects of particle dynamics in PSO. This is accomplished by using the ratio of t...

Particle swarm optimization (PSO) is a population-based stochastic optimization originating from artificial life and evolutionary computation. PSO is motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and ability of adapting to the dynamic environment make PSO b...