The Experts below are selected from a list of 3447 Experts worldwide ranked by ideXlab platform

Jun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • IEEE Congress on Evolutionary Computation - Multi-sub-swarm particle swarm optimization algorithm for Multimodal Function optimization
    2007 IEEE Congress on Evolutionary Computation, 2007
    Co-Authors: Jun Zhang, Deshuang Huang
    Abstract:

    This paper presents a novel multi-sub-swarm particle swarm optimization (PSO) algorithm. The proposed algorithm can effectively imitate a natural ecosystem, in which the different sub-populations can compete with each other. After competing, the winner will continue to explore the original district, while the loser will be obliged to explore another district. Four benchmark Multimodal Functions of varying difficulty are used as test Functions. The experimental results show that the proposed method has a stronger adaptive ability and a better performance for complicated Multimodal Functions with respect to other methods.

  • a novel adaptive sequential niche technique for Multimodal Function optimization
    Neurocomputing, 2006
    Co-Authors: Jun Zhang, Deshuang Huang
    Abstract:

    Abstract This paper proposes a novel adaptive sequential niche particle swarm optimization (ASNPSO) algorithm, which uses multiple sub-swarms to detect optimal solutions sequentially. In this algorithm, the hill valley Function is used to determine how to change the fitness of a particle in a sub-swarm run currently. This algorithm has strong and adaptive searching ability. The experimental results show that the proposed ASNPSO algorithm is very effective and efficient in searching for multiple optimal solutions for benchmark test Functions without any prior knowledge.

Arthur C Sanderson - One of the best experts on this subject based on the ideXlab platform.

  • Multimodal Function optimization using minimal representation size clustering and its application to planning multipaths
    Evolutionary Computation, 1997
    Co-Authors: Cem Hocaoglu, Arthur C Sanderson
    Abstract:

    A novel genetic algorithm (GA) using minimal representation size cluster (MRSC) analysis is designed and implemented for solving Multimodal Function optimization problems. The problem of Multimodal Function optimization is framed within a hypothesize-and-test paradigm using minimal representation size (minimal complexity) for species formation and a GA. A multiple-population GA is developed to identify different species. The number of populations, thus the number of different species, is determined by the minimal representation size criterion. Therefore, the proposed algorithm reveals the unknown structure of the Multimodal Function when a priori knowledge about the Function is unknown. The effectiveness of the algorithm is demonstrated on a number of Multimodal test Functions. The proposed scheme results in a highly parallel algorithm for finding multiple local minima. In this paper, a path-planning algorithm is also developed based on the MRSC-GA algorithm. The algorithm utilizes MRSC_GA for planning paths for mobile robots, piano-mover problems, and N-link manipulators. The MRSC_GA is used for generating multipaths to provide alternative solutions to the path-planning problem. The generation of alternative solutions is especially important for planning paths in dynamic environments. A novel iterative multiresolution path representation is used as a basis for the GA coding. The effectiveness of the algorithm is demonstrated on a number of two-dimensional path-planning problems.

Deshuang Huang - One of the best experts on this subject based on the ideXlab platform.

  • IEEE Congress on Evolutionary Computation - Multi-sub-swarm particle swarm optimization algorithm for Multimodal Function optimization
    2007 IEEE Congress on Evolutionary Computation, 2007
    Co-Authors: Jun Zhang, Deshuang Huang
    Abstract:

    This paper presents a novel multi-sub-swarm particle swarm optimization (PSO) algorithm. The proposed algorithm can effectively imitate a natural ecosystem, in which the different sub-populations can compete with each other. After competing, the winner will continue to explore the original district, while the loser will be obliged to explore another district. Four benchmark Multimodal Functions of varying difficulty are used as test Functions. The experimental results show that the proposed method has a stronger adaptive ability and a better performance for complicated Multimodal Functions with respect to other methods.

  • a novel adaptive sequential niche technique for Multimodal Function optimization
    Neurocomputing, 2006
    Co-Authors: Jun Zhang, Deshuang Huang
    Abstract:

    Abstract This paper proposes a novel adaptive sequential niche particle swarm optimization (ASNPSO) algorithm, which uses multiple sub-swarms to detect optimal solutions sequentially. In this algorithm, the hill valley Function is used to determine how to change the fitness of a particle in a sub-swarm run currently. This algorithm has strong and adaptive searching ability. The experimental results show that the proposed ASNPSO algorithm is very effective and efficient in searching for multiple optimal solutions for benchmark test Functions without any prior knowledge.

Yong Liang - One of the best experts on this subject based on the ideXlab platform.

  • genetic algorithm with adaptive elitist population strategies for Multimodal Function optimization
    Applied Soft Computing, 2011
    Co-Authors: Yong Liang, Kwongsak Leung
    Abstract:

    This paper introduces a new technique called adaptive elitist-population search method. This technique allows unimodal Function optimization methods to be extended to efficiently explore multiple optima of Multimodal problems. It is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and a novel direction dependent elitist genetic operators. Incorporation of the new Multimodal technique in any known evolutionary algorithm leads to a Multimodal version of the algorithm. As a case study, we have integrated the new technique into Genetic Algorithms (GAs), yielding an Adaptive Elitist-population based Genetic Algorithm (AEGA). AEGA has been shown to be very efficient and effective in finding multiple solutions of complicated benchmark and real-world Multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including rough and stepwise Multimodal Functions. Empirical results are also compared with other Multimodal evolutionary algorithms from the literature, showing that AEGA generally outperforms existing approaches.

  • adaptive elitist population based genetic algorithm for Multimodal Function optimization
    Genetic and Evolutionary Computation Conference, 2003
    Co-Authors: Kwongsak Leung, Yong Liang
    Abstract:

    This paper introduces a new technique called adaptive elitist-population search method for allowing unimodal Function optimization methods to be extended to efficiently locate all optima of Multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a Multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the Multimodal technique, yielding an adaptive elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark Multimodal optimization problems.

Kwongsak Leung - One of the best experts on this subject based on the ideXlab platform.

  • genetic algorithm with adaptive elitist population strategies for Multimodal Function optimization
    Applied Soft Computing, 2011
    Co-Authors: Yong Liang, Kwongsak Leung
    Abstract:

    This paper introduces a new technique called adaptive elitist-population search method. This technique allows unimodal Function optimization methods to be extended to efficiently explore multiple optima of Multimodal problems. It is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and a novel direction dependent elitist genetic operators. Incorporation of the new Multimodal technique in any known evolutionary algorithm leads to a Multimodal version of the algorithm. As a case study, we have integrated the new technique into Genetic Algorithms (GAs), yielding an Adaptive Elitist-population based Genetic Algorithm (AEGA). AEGA has been shown to be very efficient and effective in finding multiple solutions of complicated benchmark and real-world Multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including rough and stepwise Multimodal Functions. Empirical results are also compared with other Multimodal evolutionary algorithms from the literature, showing that AEGA generally outperforms existing approaches.

  • adaptive elitist population based genetic algorithm for Multimodal Function optimization
    Genetic and Evolutionary Computation Conference, 2003
    Co-Authors: Kwongsak Leung, Yong Liang
    Abstract:

    This paper introduces a new technique called adaptive elitist-population search method for allowing unimodal Function optimization methods to be extended to efficiently locate all optima of Multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a Multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the Multimodal technique, yielding an adaptive elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark Multimodal optimization problems.