Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Operations Research

Swarm intelligence (SI) is considered one of the most popular computational intelligence paradigms. It originated from the study of colonies, or swarms of social organisms. Studies of the social behavior of organisms (individuals) in swarms prompted the design of very efficient optimization and clustering algorithms used to solve difficult optimization problems by simulating natural evolution over populations of candidate solutions. Among the different works inspired by swarm, the ant colony optimization and particle swarm optimization metaheuristics are probably themost successful and popular techniques on which we focused in this thesis. This thesis introduces a hybrid approach of particle swarm optimization (PSO) and ant colony optimization (ACO) for solving nonlinear optimization problem. The proposed algorithm consists of two phases; the first phase use ACO to find satisfied solution, in the second phase the solution is improved by PSO. The main objective of the second phase is starting with feasible solution instead of starting with random solution and improves these feasible solution

Issued also as CD