A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO) algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for m...

Digital filters play a major role in signal processing that are employed many applications such as control systems, audio or video noise reduction and different systems for communication. In this regard, FIR because of frequency stability linearity their phase response. filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm its variants ...

This paper proposes a new Shrinking Hypersphere PSO (SHPSO) for continuous function optimization. The global best and personal best fly in the search space in the form of a hypersphere instead of particles. The hyperspheres keep on shrinking as the iterations proceed and the velocity and position update equations are applied at each iteration. The theoretical convergence of the SHPSO is proved....

Although Particle Swarm Optimizers (PSO) have been successfully used in a wide variety of continuous optimization problems, their use has not been as widespread in discrete optimization problems, particularly when adopting non-binary encodings. In this chapter, we discuss three PSO variants (which are applied on a specific scheduling problem: the Single Machine Total Weighted Tardiness): a Hybr...

In this paper, well-known PSO algorithms reported in the literature for solving continuous function optimization problems were comparatively evaluated by considering real world data clustering problems. Data clustering problems are solved, by considering three performance clustering metrics such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The result...

Particle swarm optimization (PSO) method is relatively new, simple yet powerful and widely used in applied fields. However PSO does not seem to have made an impact in mainstream statistical applications hitherto. We propose variants of the PSO method to find optimal experimental designs for both linear and nonlinear models in a novel way. We show that the PSO method can simply generate many typ...

Although evident progress and considerable achievements have been attained in developing a new particle swarm optimization (PSO) algorithm, successfully balancing the exploration and exploitation capabilities of PSO to determine high-quality solutions for complex optimization problems remains a fundamental challenge. In this study, we propose a new PSO variant, namely, adaptive division of labo...

The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO essentially unconstrained requires constraint handling techniques (CHTs) to solve constrained problems (COPs). For this purpose, we integrate two CHTs, superiority feasibility (SF) violation constraint-handling (VCH)...

This paper presents a modification of tlte panicle swarm optimization algorithm (PSO) intended to combat the problem ofpremature convergence observed in many applications of PSO. The proposed new algorithm moves particles towards neorby particles of higher fitness, instead of amacting each panicle towards just the best position discovered so far by any particle. This is accomplished by using th...

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations...