A particle swarm optimization (PSO) based algorithm for finding the Pareto solutions of multiobjective design problems is proposed. To enhance the global searching ability of the available PSOs, a novel formula for updating the particles' velocity and position, as well as the introduction of craziness, are reported. To handle a multiobjective design problem using the improved PSO, a new fitness assignment mechanism is proposed. Moreover, two repositories, together with the age variables for their members, are introduced for storing and selecting the previous best positions of the particle as well as that of its companions. Besides, the use of age variables to enhance the diversity of the solutions is also described. The proposed method is tested on two numerical examples with promising results.Index Terms-Inverse problem, multiobjective optimal algorithm, optimal design, particle swarm optimization (PSO).

Based on the refinement successes to particle swarm optimization (PSO) methods, which include, namely, the introduction of an age variable, the proposal of new selection strategies to find the best solutions of the particle as well as for its neighbors, the design of a novel formula for velocity updating, the incorporation of an intensification search phase, and so on, an improved PSO method is presented. The experimental results reported indicate that the refined pinpointing search ability and the global search ability of the proposed method are significantly improved when compared to those of conventional PSOs.Index Terms-Global optimization, metaheuristic algorithm, particle swarm optimization (PSO), stochastic method.

To address fully multiobjective synthesis problems in antenna engineering, a vector tabu search algorithm is proposed to produce a good distribution of the searched Pareto solutions to successfully optimize a completely nonuniform 19-element linear antenna array.

To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus redefined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm. Index Terms-Genetic algorithm (GA), global optimization, inverse problem, population based incremental learning (PBIL) method.

Based on the success in the design of a new global search procedure on the development of a novel trail updating mechanism and the introduction of an elitist strategy to available ant colony optimization (ACO) methods, an improved ACO algorithm is proposed. In order to facilitate the implementation of the search procedure, the available local search phase is simplified also. The algorithm is tested on a mathematical function and an inverse problem, and its performances are compared with those of other well designed methods.Index Terms-Ant colony optimization (ACO) method, heuristic algorithm, inverse problem, optimal design.