This paper presents use of Particle Swarm Optimization (PSO) algorithm introduced by Kennedy and Eberhart [1] for solving Integer and Mixed Integer Optimization problems. In PSO, The potential solutions, called particles, are flown through the problem space by learning from the current optimal particle and its memory. PSO is started with a group of feasible solutions and a feasibility function ...

This paper presents the preliminary studies for the creation of a new tool to assist in medical diagnostic. The tool will help in the analysis of 2D-PAGE images. In order to create a 2D-PAGE image of an ideal patient—the patient could be healthy or ill—the tool will help us in the creation of an image that facilitates and speeds up future diagnostics. The creation of a master image has motivate...

Intelligent heuristic optimization methods have increasingly attracted the attentions and interests of many scholars in recent years. Such as genetic algorithm, ant colony algorithm, particle swarm optimization, simulated annealing, etc.. They have become effective tools to solve the TSP and other NP-hard combinatorial optimization problems. The particle swarm optimization (PSO) algorithm is a ...

Particle swarm optimisation (PSO) was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995) inspired by the collective behaviour of natural birds or fish. PSO is a stochastic optimisation technique that uses a behaviour of population composed by many search points called particle. In spite of easy implementation in computer algorithms, it is well known as a powerful numerical opt...

Particle swarm optimization, PSO, is an evolutionary computation technique inspired in the behavior of bird flocks. PSO algorithms were first introduced by Kennedy & Eberhart (1995) for optimizing continuous nonlinear functions. The fundamentals of this metaheuristic approach rely on researches where the movements of social creatures were simulated by computers (Reeves, 1983; Reynolds, 1987; He...

Present study was undertaken to estimate the magnitude of genotype × environment interaction (GEI) and identify stability improved high-yielding varieties Indian mustard (Brassica juncea L. Czerns & coss.). The 57 genotypes were evaluated in RBD along with three replications during Rabi crop seasons environments. Analysis variance on 14 traits carried out individually as well pooled over Yi...