Swarm intelligence (SI) is widely used in many complex optimization problems. It is a collective behavior of social systems such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This paper presents a detailed overview of Particle Swarm Optimization (PSO), its variants and hybridization of PSO with Bee Algorithm (BA). This paper also surveys various SI techniques p...

Particle swarm optimization (PSO) is one of the most famous swarm-based techniques inspired by nature. Due to its properties flexibility and easy implementation, there an enormous increase in popularity this nature-inspired technique. has gained prompt attention from every field researchers. Since origin 1995 till now, researchers have improved original varying ways. They derived new versions i...

Particle swarm methods are inspired from the dynamics of social interaction and employ information sharing to seek solutions to difficult optimisation problems. In this paper we introduce an approach that combines ideas from particle swarm optimisation (PSO) and the theory of nonextensive statistical mechanics. We develop two algorithms that adopt this approach and conduct an experimental study...

There are different variants of Particle Swarm Optimization (PSO) algorithm such as Adaptive Particle Swarm Optimization (APSO) and Particle Swarm Optimization with an Aging Leader and Challengers (ALC-PSO). These algorithms improve the performance of PSO in terms of finding the best solution and accelerating the convergence speed. However, these algorithms are computationally intensive. The go...

Particle Swarm Optimization (PSO) has been extensively used in recent years for the optimization of nonlinear optimization problems. Two of the most popular variants of PSO are PSO-W (PSO with inertia weight) and PSO-C (PSO with constriction factor). Typically particles in swarm use information from global best performing particle, gbest and their own personal best, pbest. Recently, studies hav...

Since 2006, three successive standard PSO versions have been put on line on the Particle Swarm Central [10], namely SPSO 2006, 2007, and 2011. The basic principles of all three versions can be informally described the same way, and in general, this statement holds for almost all PSO variants. However, the exact formulae are slightly di erent, because they took advantage of latest theoretical an...

This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization using different distributions and Low-discrepancy sequences. These algorithms are applied to various benchmark problems including unimodal, multimodal, noisy fu...

Generality is one of the main advantages of heuristic algorithms, as such, multiple parameters are exposed to the user with the objective of allowing them to shape the algorithms to their specific needs. Parameter selection, therefore, becomes an intrinsic problem of every heuristic algorithm. Selecting good parameter values relies not only on knowledge related to the problem at hand, but to th...

This paper proposes a novel application of a chaos particle swarm optimization (PSO) algorithm for tracking a maximum power point (MPP) of a solar photovoltaic (PV) panel under varying atmospheric conditions. Solar PV cells have a nonlinear V-I characteristic with a distinct MPP which depends on environmental factors such as temperature and irradiation. In order to continuously harvest maximum ...

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present new variants of Particle Swarm Optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to extend the single population PSO and Charged Particle Swarm Optimization (CPSO) methods by constructi...