A new multi-objective particle swarm optimization algorithm is presented. The proposed multi-objective particle swarm optimization algorithm is based on a MaxiMin technique previously proposed for a multi-objective genetic algorithm. The technique is applied to optimize a benchmark function set and to the design of PID controllers regarding the objectives of set-point tracking and output distur...

In the present work, we introduce the particle swarm optimization called (PSO in short) to avoid the Runge’s phenomenon occurring in many numerical problems. This new approach is tested with some numerical examples including the generalized integral quadrature method in order to solve the Volterra’s integral equations. Keywords—Integral equation, particle swarm optimization, Runge’s phenomenon.

Optimization algorithms are proposed to tackle different complex problems in different areas. In this paper, we firstly put forward a new memetic evolutionary algorithm, named Monkey King Evolutionary (MKE) Algorithm, for global optimization. Then we make a deep analysis of three update schemes for the proposed algorithm. Finally we give an application of this algorithm to solve least gasoline ...

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the varia...

Based on the combination of the particle swarm algorithm and multiplier penalty function method for the constraint conditions, this paper proposes an improved hybrid particle swarm optimization algorithm which is used to solve nonlinear constraint optimization problems. The algorithm converts nonlinear constraint function into no-constraints nonlinear problems by constructing the multiplier pen...

the simultaneous optimization of multiple responses is an important problem in the design of industrial processes in order to achieve improved quality. in this paper, we present a new metaheuristic approach including simulated annealing and particle swarm optimization to optimize all responses simultaneously. for the purpose of illustration and comparison, the proposed approach is applied to tw...

The increased nature of email spam with the use of urge mailing tools prompt the need for detector generation to counter the menace of unsolocited email. Detector generation inspired by the human immune system implements particle swarm optimization (PSO) to generate detector in negative selection algorithm (NSA). Outlier detectors are unique features generated by local outlier factor (LOF). The...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

A new methodology for Emergent System Identification is proposed in this paper. The new method applies the self-organizing Group Method of Data Handling (GMDH) functional networks, Particle Swarm Optimization (PSO), and Genetic Programming (GP) that is effective in identifying complex dynamic systems. The focus of the paper will be on how Particle Swarm Optimization (PSO) is applied within Grou...

In this paper, a new approach is proposed for the optimum design of single-phase induction motor. By using the classical design equations and the evolutionary algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Modified Particle Swarm Optimization (MPSO), a Single Phase Induction Motor (SPIM) was designed with the maximum efficiency. The Finite Element Method (FEM)...