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Metaheuristics Based on a Set of Solutions

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Abstract

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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Dates and versions

hal-03320847 , version 1 (16-08-2021)

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Nacima Labadie, Christian Prins, Caroline Prodhon. Metaheuristics Based on a Set of Solutions. Metaheuristics for Vehicle Routing Problems, Volume 3, 2016, 9781848218116. ⟨10.1002/9781119136767.ch4⟩. ⟨hal-03320847⟩

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