This chapter describes metaheuristics evolving a set of solutions and generating new solutions by either combining existing ones or by making them cooperate through a learning process for vehicle routing problems. It presents distinguished population‐based approaches, which combine solutions selected from a population stored in memory from swarm methods such as particle swarm optimization (PSO) or ant colony optimization (ACO) based on a cooperation of homogenous agents in their environment. Genetic algorithms (GAs) are subsumed on population‐based approaches. Three variants are identified, namely the basic version (GA), its advanced variant using local search procedures, called memetic algorithm (MA), and a further enhanced method which includes a population management mechanism (MA|PM). Scatter search (SS) also follows a reproduction scheme that combines solutions in a systematic way. Another metaheuristic approach, namely path relinking (PR), uses two solutions from a population to generate new ones, but without crossover operators.


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    Title :

    Metaheuristics Based on a Set of Solutions


    Contributors:


    Publication date :

    2016-03-30


    Size :

    31 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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