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In this paper, a neighborhood mutation strategy is proposed and integrated with various niching differential evolution (DE) algorithms to solve multimodal optimization problems. Although variants of DE are highly effective in locating a single global optimum, no DE variant performs competitively when solving multi-optima problems. In the proposed neighborhood based differential evolution, the mutation is performed within each Euclidean neighborhood. The neighborhood mutation is able to maintain the multiple optima found during the evolution and evolve toward the respective global/local optimum. To test the performance of the proposed neighborhood mutation DE, a total of 29 problem instances are used. The proposed algorithms are compared with a number of state-of-the-art multimodal optimization approaches and the experimental results suggest that although the idea of neighborhood mutation is simple, it is able to provide better and more consistent performance over the state-of-the-art multimodal algorithms. In addition, a comparative survey on niching algorithms and their applications are also presented. [ABSTRACT FROM AUTHOR]

The global best (gbest) or local best (lbest) of every particle in state-of-the-art multi-objective particle swarm optimization (MOPSO) implementations is selected from the non-dominated solutions in the external archive. This approach emphasizes the elitism at the expense of diversity when the size of the current set of non-dominated solutions in the external archive is small. This article proposes that the gbests or lbests should be chosen from the top fronts in a non-domination sorted external archive of reasonably large size. In addition, a novel two local bests (lbest) based MOPSO (2LB-MOPSO) version is proposed to focus the search around small regions in the parameter space in the vicinity of the best existing fronts unlike the current MOPSO variants in which the pbest and gbest (or lbest) of a particle can be located far apart in the parameter space thereby potentially resulting in a chaotic search behaviour. Comparative evaluation using 19 multi-objectives test problems and 11 state-of-the-art multi-objective evolutionary algorithms ranks overall the 2LB-MOPSO as the best while two state-of-the-art MOPSO algorithms are ranked the worst with respect to other multi-objective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

A method of using particle swarm optimisation (PSO) algorithms to optimise the element spacing and lengths of Yagi--Uda antennas is presented. SuperNEC, an object-oriented version of the numerical electromagnetic code (NEC-2) is used to evaluate the performance of various Yagi--Uda antenna designs. In order to show the capabilities of the PSO algorithm in Yagi--Uda antenna design, three different antenna design cases are optimised for various performance specifications. The three objectives considered are gain only, gain and input impedance only, and gain, input impedance and relative sidelobe level (rSLL). To alleviate the premature convergence problem of PSO, a novel learning strategy is employed. Each design problem is optimised using three variants of PSO algorithms, namely the modified PSO, fitness-distance ratio PSO (FDR-PSO), and comprehensive learning PSO (CLPSO). For the purpose of comparison and benchmarking, equally spaced arrays, genetic algorithm optimised antenna design, and computational intelligence optimised antenna design are considered. The results clearly show that the CLPSO is a robust and useful optimisation tool for designing Yagi antennas for the desired target specifications. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel mixed-integer optimization formulation for the optimal design of a reconfigurable antenna array with quantized phase excitations, which is solved by using the generalized generation gap (G3) model genetic algorithm (GA). To reduce effect of mutual coupling between the antenna-array elements, the amplitude excitations' dynamic-range ratio is minimized. The simulation experiments* clearly show an improved performance of the discrete realization of the phase-excitation values, as compared to the continuous realization and subsequent quantization. The effect of quantization on the continuous formulation of phase excitations is also demonstrated. The proposed mixed-integer formulation can be used in practice for the optimal design of dual-beam antenna arrays. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 45: 461–465, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20853 [ABSTRACT FROM AUTHOR]

Introduces a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. ARG representation; Application of GA for ARG matching; Summary of the pose-clustering algorithm; Presentation of experimental results and discussion.