This paper presents an effective hybrid coevolutionary particle swarm optimization algorithm for solving constrained engineering design problems, which is based on simulated annealing (SA) , employing the notion of co-evolution to adapt penalty factors. By employing the SAbased selection for the best position of particles and swarms when updating the velocity in co-evolutionary particle swarm o...

In practical situations, distribution network loads are the mixtures of residential, industrial, and commercial types. This paper presents a hybrid optimization algorithm for the optimal placement of shunt capacitor banks in radial distribution networks in the presence of different voltage-dependent load models. The algorithm is based on the combination of Genetic Algorithm (GA) and Binary Part...

This paper proposes a refined version of particle swarm optimization technique for the optimum design of steel structures. Swarm is composed of a number of particles and each particle in the swarm represents a candidate solution of the optimum design problem. Design constraints in accordance with ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution) are imposed by t...

Particle swarm optimization (PSO) is a type of intelligence algorithm that frequently used to resolve specific global problems due its rapid convergence and ease operation. However, PSO still has certain deficiencies, such as poor trade-off between exploration exploitation premature convergence. Hence, this paper proposes dual-stage hybrid learning particle (DHLPSO). In the algorithm, iterative...

A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of multi-objective optimization (moo) had arisen several years ago. due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by swarm intelligence (si) techniques. particle swarm optimization (pso) has ...

In this paper, we propose a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and on Simulated Annealing (SA) for reducing memory energy consumption in embedded systems. Our hybrid algorithm outperforms the Tabu Search (TS) approach. In fact, nearly from 76% up to 98% less energy consumption is recorded.

In this paper, we propose a hybridization of electromagnetism-like mechanism (EM) and particle swarm optimization algorithm (PSO) algorithms to design the proposed functional-link based Petri recurrent fuzzy neural system (FLPRFNS) for application of nonlinear system control. The FLPRFNS has a TSK-type fuzzy consequent part which uses functional-link based orthogonal basis functions and a Petri...