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...

This paper suggests novel optimal approach by progressive mapping search method (PMSM) of neural network aided particle swarm optimization (PSO) that can obtain global optimal solution easily and speed searching time up by PMSM. The PMSM by NN and PSO has an important role as navigation when PSO is going to search all areas to have an optimal solution, it can help to increase searching capabili...

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...

Particle Swarm Optimization (PSO) has shown its good search ability in many optimization problems. But PSO easily gets trapped into local optima while dealing with complex problems due to lacks in diversity. In this work, we proposed an improved PSO, namely PSO-APMLB, in which adaptive polynomial mutation strategy is employed in local best of particles to introduce diversity in the swarm space....

This study proposes a multi-objective optimization model of two cascade reservoirs in the Upper Yellow River basin for increasing social well-beings in general while simultaneously mitigating ice/flood threats. We first develop a strategy of dimensionality reduction and constraint transformation to largely diminish the complexity of the optimization system and next propose a novel search method...

Particle Swarm Optimization (PSO) is a population based optimal method and very simple in both theory and numerical implementation. Nowadays, PSO has been recognized as a paradigm for numerical optimizations; however, PSO is easily trapped into a local optimum when solving multidimensional and complex problems. In order to overcome this difficulty, this paper presents a modified PSO with a dyna...

In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance...

This paper investigates into hybridization between PSO and self-adaptive evolutionary programming techniques for solving economic dispatch (ED) problem with non-smooth cost curves where conventional gradient based methods are in-applicable. The convergence capability of evolutionary programming technique is enhanced with hybridization of self-adaptive evolutionary programming technique with PSO...

Abstract— The aim of this paper is to study the tuning of a PID controller using swarm optimization techniques. In this paper, comparative performance of PSO and BF-PSO based PID controller is analyzed. PSO algorithm converges rapidly during the initial stages of a global search, but around global optimum, the search process slows down. In order to overcome this problem and to further enhance t...

Distant luminous quasars provide important information on the growth of the first supermassive black holes, their host galaxies and the epoch of reionization. The identification of quasars is usually performed through detection of their Lyman-α line redshifted to 0.9 microns at z > 6.5. Here, we report the discovery of a very Lyman-α luminous quasar, PSO J006.1240 + 39.2219 at redshift z = 6.61...