ARPN Journal of Science and Technology::A Hybrıd Partıcle Swarm Genetıc Algorıthm (Psoga) For N-Queen Problem

Evolutionary algorithms have proven to be robust tools in data processing for modeling dynamic, non-linear and complex processes due to their flexible mathematical structure to yield optimal results even with imprecise, ambiguity and noise at its input. The study is a soft-computing heuristic-based, hybrid approach that aims to provide computational intelligence via solving optimization task. The hybrid will become a veritable algorithm for computing dynamic and discrete states for multipoint search in CSPs tasks with application areas to include image and video analysis, network design and construction, communication, simulation, multiprocessor load balancing, OS task scheduling/resource allocation, parallel processing, power generation, medicine, economics, security/military, fault diagnosis and recovery, forecasting and predictions, data mining, signal processing, cloud and clustering computing to mention a few. The hybrid algorithm: (a) via particle swarm optimization, places a number of simple agents or particles in space so that each can evaluate the objective, fitness function. Thus, each particle determines its movement around the space by combining some aspects of its own current and best locations with those of other members of the swarm, along with some random perturbation. The next iteration occurs after all the particles have moved to their new locations with updated velocities and the entire swarm will tend to move closer to optimal, and then (b) via genetic algorithm (GA), it defines the swarm of particle via three (3) operators (selection, crossover and mutation) to result in unique values. Crossover and mutation factors will prevent the particle best (pbest) from entrapment at local minima and study contributes in its design of a hybrid PSOGA model for implementation of an N-Queen classical CSP task to provide computational intelligence.