PROJECT TITLE :

Simple Probabilistic Population-Based Optimization

ABSTRACT:

A generic theme is proposed for planning and classifying easy probabilistic population-primarily based optimization (SPPBO) algorithms that use principles from population-primarily based ant colony optimization (PACO) and simplified swarm optimization (SSO) for solving combinatorial optimization problems. The theme, known as SPPBO, identifies completely different varieties of populations (or archives) and their influence on the construction of recent solutions. The theme is used to point out how SSO can be custom-made for solving combinatorial optimization problems and the way it is related to PACO. Moreover, many new variants and combos of these two metaheuristics are generated with the proposed scheme. An experimental study is done to guage and compare the influence of various population sorts on the optimization behavior of SPPBO algorithms, when applied to the traveling salesperson drawback and also the quadratic assignment problem.