The objective of this thesis is to investigate how to improve Particle Swarm Optimization by hybridization of stochastic search heuristics and by a Self-Organized Criticality extension. The thesis will describe two hybrid models extending Particle Swarm Optimization with two aspects from Evolutionary Algorithms, recombination via breeding and gene flow restriction via subpopulations. A further ...

In this paper we theoretically investigate underlying assumptions that have been used for designing adaptive particle swarm optimization algorithms in the past years. We relate these assumptions to the movement patterns of particles controlled by coefficient values (inertia weight and acceleration coefficient) and introduce three factors, namely the autocorrelation of the particle positions, th...

in this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classifypatterns of different classes in the feature space. the introduced mutation operators and chaotic sequences allows us to overcomethe problem of early convergence into a local minima associated with particle swarm optimization algorithms. that is, the mutationope...

This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for selfadaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of conver...

Particle swarm optimization (PSO) is a kind of evolutionary algorithm to find optimal solutions for continuous optimization problems. Updating kinetic equations for particle swarm optimization algorithm are improved to solve traveling salesman problem (TSP) based on problem characteristics and discrete variable. Those strategies which are named heuristic factor, reversion mutant and adaptive no...

This paper presents a comparative analysis of continuously stirred tank reactor (CSTR) control based on adaptive control and optimal tuning of PID control based on particle swarm optimization. In the design of adaptive control, Model reference adaptive control (MRAC) scheme is used, in which the adaptation law have been developed by MIT rule & Lyapunov’s rule. In PSO control parameters of PID c...

A Multi Objective based Fitness Function (MOFF) is proposed for the optimum planning of multiple Solar Distributed Generation (SDG) and DSTATCOM with radial distribution network (RDN) reconfiguration impact techno-economic environmental benefit improvement. The Adaptive-Particle Swarm Optimization (APSO) Teaching-Learning Based techniques (TLBO) are employed to accomplish this work. In MOFF, Ac...

This paper presents asynchronous particle swarm optimization (APSO) applied to the time-domain inverse scattering problems of two-dimensional metallic cylinder buried in slab medium. For this study the finite-difference time-domain (FDTD) is employed for the analysis of the forward scattering part, while for the APSO is applied for the reconstruction of the two-dimensional metallic cylinder bur...

The Optimal Power Flow (OPF) plays an important role in power system operation and control due to depleting energy resources, and increasing power generation cost and ever growing demand for electric energy. As the size of the power system increases, load may be varying. The generators should share the total demand plus losses among themselves. The sharing should be based on the fuel cost of th...

The paper illustrates the application of a combined adaptive particle swarm optimization (A-PSO) algorithm and the finite strip method (FSM) to the lay-up optimization of symmetrically fiber-metal laminated (FML) composite shallow shell panels for maximizing the fundamental frequency. To improve the speed of the optimization process, adaptive inertia weight was used in the particle swarm optimiz...