A quantum behaved particle swarm approach to multi-objective optimization

Many real-world optimization problems have multiple objectives that have to be optimized simultaneously. In multi-objective optimization problems, the major challenge is to find the set of solutions that achieve the best compromise with regard to the whole set of objectives. Although a great deal of effort has been devoted to solving multi-objective optimization problems, the problem is still open and the related issues still attract significant research efforts. The possibility to get a set of Pareto optimal solutions in a single run is one of the attracting and motivating features of using population based algorithms to solve optimization problems with multiple objectives. Most of the proposed approaches make use of metaheuristics. Their basic idea is to introduce the Pareto dominance concept into nature inspired algorithms such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). Quantum-behaved Particle Swarm Optimization (QPSO) is a recently proposed population based metaheuristic that relies on quantum mechanics principles. Since its inception, much effort has been devoted to developing improved versions or new applications of QPSO designed for single objective optimization. However, many of its advantages are not yet available for multi-objective optimization. In this thesis, we develop a new framework for multi-objective problems using QPSO. The contribution of the work is threefold. First a hybrid leader selection method has been developed to compute the attractor of a given particle and applied in unconstrained optimization case. Its aim is to foster convergence of the obtained Pareto fronts while maintaining good diversity. Second, an archiving strategy has been proposed to control the growth of the archive size in order to achieve a balance between the quality of solutions of an unbounded archive method and the cost effectiveness of a bounded archive method. Third, the developed framework has been further extended to handle constrained optimization problems. A comprehensive investigation of the developed framework has been carried out under different selection, archiving and constraint handling strategies. The developed framework is found to be a competitive technique to tackle this type of problems when compared against the state-of-the-art methods in multi-objective optimization.