Abstract: Constraint handling is one of the most difficult parts encountered in practical engineering design optimizations. Different kinds of methods were proposed for handling constraints namely, genetic algorithm, self-adaptive penalty approach and other evolutionary algorithms. Particle Swarm Optimization (PSO) efficiently solved most nonlinear optimization problems with inequity constraint...

Particle Swarm Optimization (PSO) with elite PSO parameters has been developed for power flow analysis under practical constrained situations. Multiple solutions of the power flow problem are useful in voltage stability assessment of power system. A method of determination of multiple power flow solutions is presented using a hybrid of Particle Swarm Optimization (PSO) and local search techniqu...

This paper introduces a new approach for designing an adaptive fuzzy model predictive control (AFMPC) using the Particle Swarm Optimization (PSO) algorithm. The system to be controlled is modeled by a Takagi-Sugeno fuzzy inference system whose parameters are identified using recursive least square algorithm. These parameters are used to calculate the objective function based on predictive appro...

This paper presents an efficient evolutionary method to optimize the gain ripple of multi-pumps photonic crystal fiber Raman amplifier using the Fuzzy Adaptive Modified PSO (FAMPSO) algorithm. The original PSO has difficulties in premature convergence, performance and the diversity loss in optimization as well as appropriate tuning of its parameters. The feasibility and effectiveness of the pro...

The flow in sewers is a complete three phase flow (air, water and sediment). The mechanism of sediment transport in sewers is very important. In other words, the passing flow must able to wash deposited sediments and the design should be done in an economic and optimized way. In this study, the sediment transport process in sewers is simulated using a hybrid model. In other words, using the Ada...

Among variety of meta-heuristic population-based search algorithms, particle swarm optimization (PSO) with adaptive inertia weight (AIW) has been considered as a versatile optimization tool, which incorporates the experience of the whole swarm into the movement of particles. Although the exploitation ability of this algorithm is great, it cannot comprehensively explore the search space and may ...

In this paper, an improved method based on evolutionary algorithm for speech signal denoising is proposed. In this approach, the stochastic global optimization techniques such as Artificial Bee Colony(ABC), Cuckoo Search (CS)algorithm, and Particle Swarm Optimization (PSO) technique are exploited for learning the parameters of adaptive filtering function required for optimum performance. It was...

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...

This paper presents a novel design for mobile robot using particle swarm optimization (PSO) and adaptive NN control. The adaptive NN control strategy guarantees that robot with nonholonomic constraints can follow smooth trajectories. Based on this property, a PSO algorithm for path planning is proposed. The path planning generates smooth path with low computational cost to avoid obstacles, so t...

-Particle Swarm Optimization (PSO) has received increased attention in the evolutionary computation fields recently. In the paper, we proposed Adaptive constriction factor for Location-related Particle Swarm (ALPS) that is shown to be superior when compared with the existing PSO algorithm. We adapt a technique of overcoming complex problems with PSO. This is accomplished by using the ratio of t...