Particle Swarm Optimization (PSO) is a swarm intelligence optimization method inspired from birds’ flocking or fish schooling. Many improved versions of PSO are reported in literature, including some by the authors. Original as well as improved versions of PSO have proven their applicability to various fields like science, engineering and industries. Economic dispatch (ED) problem is one of the...

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. The proposed new algorithm moves particles towards nearby particles of higher fitness, instead of attracting each particle towards just the best position discovered so far by any particle. This is accomplished by usin...

In a Wireless Sensor Network (WSN) accurate location of target node is highly desirable as it has strong impact on overall performance of the WSN. This paper proposes the application of different migration variants of Biogeography-Based Optimization (BBO) algorithm and Particle Swarm Optimization (PSO) for distributed optimal localization of randomly deployed sensors for different ranges. Bioge...

Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by Swarm Intelligence (SI) techniques. Particle Swarm Optimization (PSO) has ...

Inspired by the phenomenon of symbiosis in natural ecosystems a multi-swarm cooperative particle swarm optimizer (MCPSO) is proposed as a new fuzzy modeling strategy for identification and control of non-linear dynamical systems. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms execute particle swarm optimization (PSO) or its variants independentl...

Early studies in particle swarm optimization (PSO) algorithm reveal that the social and cognitive components of swarm, i.e. memory swarm, tend to distribute around the problem's optima. Motivated by these findings, we propose a two-layer PSO with intelligent division of labor (TLPSO-IDL) that aims to improve the search capabilities of PSO through the evolution memory swarm. The evolution in TLP...

The problem of finding more than one optimum of a fitness function has been addressed in evolutionary computation using a wide variety of algorithms, including particle swarm optimization (PSO). Several variants of the PSO algorithm have been developed to deal with this sort of problem with different degrees of success, but a common drawback of such approaches is that they normally add new para...

We introduce a new variant for the constriction coefficient model of the established particle swarm optimization (PSO) algorithm. The new variant stands between the synchronous and asynchronous version of PSO, combining their operation regarding the update and evaluation frequency of the particles. Yet, the proposed variant has a unique feature that distinguishes it from other approaches. Speci...

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algori...

Particle swarm optimization (PSO) is a novel swarm intelligent algorithm inspired by fish schooling and birds flocking. Due to the complex nature of engineering optimization tasks, the standard version can not always meet the optimization requirements. Therefore, in this paper, a new group decision mechanism is introduced to PSO to enhance the escaping capability from local optimum. Furthermore...