Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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08 Oct 2012
TL;DR: By combining the two algorithms, a hybrid algorithm is proposed to solve the vehicle routing problem, avoiding the disadvantages of long time searching, easily falling into local optimal solution in ACO and the shortcomings of iterative redundancy, inefficiency in GA.
Abstract: Ant Colony Optimization (ACO) algorithm and genetic algorithms (GA) are two commonly used methods dealing with vehicle route optimizing. According to the characteristics of the two methods, by combining the two algorithms, a hybrid algorithm is proposed to solve the vehicle routing problem, avoiding the disadvantages of long time searching, easily falling into local optimal solution in ACO and the shortcomings of iterative redundancy, inefficiency in GA. Some experimental results prove that the hybrid optimization algorithm (HOA) is feasible and efficient in solving the problem of vehicle route optimization in logistics distribution.
3 citations
Dissertation•
01 Jan 2002
3 citations
Journal Article•
TL;DR: The paper introduces basic particle swarm optimization and analyses its use in the traveling salesman problem and studies how to use PSO in solving discrete optimization problems.
Abstract: The paper introduces basic particle swarm optimization and analyses its use in the traveling salesman problem.Particle Swarm Optimization(PSO)is a new kind of evolutionary computation,which has been proved to be a powerful global optimization method.In the optimization field,PSO is suitable for continuous optimization,and it is rarely used in discrete optimization.Therefore,the paper studies how to use PSO in solving discrete optimization problems.And some experiments are done and the results of the experiments are analyzed.
3 citations
Journal Article•
3 citations
TL;DR: This paper proposes a method to enhance the Mixed Nash Extremal Optimization algorithm by reducing the number of payoff function evaluations, which is efficient, with results better or just as good as other state-of-the-art methods.
Abstract: Game theory based methods designed to solve the problem of community structure detection in complex networks have emerged in recent years as an alternative to classical and optimization based approaches. The Mixed Nash Extremal Optimization uses a generative relation for the characterization of Nash equilibria to identify the community structure of a network by converting the problem into a non-cooperative game. This paper proposes a method to enhance this algorithm by reducing the number of payoff function evaluations. Numerical experiments performed on synthetic and real-world networks show that this approach is efficient, with results better or just as good as other state-of-the-art methods.
3 citations