Abstract
One of the possible approaches to solving difficult optimization problems is applying population-based metaheuristics. Among such metaheuristics, there is a special class where searching for the best solution is based on the collective behavior of decentralized, self-organized agents. This study proposes an approach in which a swarm of agents tries to improve solutions from the population of solutions. The process is carried out in parallel threads. The proposed algorithm—based on the mushroom-picking metaphor—was implemented using Scala in an Apache Spark environment. An extended computational experiment shows how introducing a combination of simple optimization agents and increasing the number of threads may improve the results obtained by the model in the case of TSP and JSSP problems.
Highlights
Pioneering work in the area of the population-based metaheuristics included genetic algorithms (GA) [1], evolutionary computations—EC [2], particle swarm optimization (PSO) [3] and its two offspring—ant colony optimization [4] and bee colony algorithms (BCA) [5]
We propose to tackle traveling salesman problem (TSP) and job shop scheduling problem (JSSP) instances with a dedicated parallel metaheuristic algorithm implemented in Scala and executed using the Apache Spark environment
The study proposes an approach for solving the TSP and JSSP instances in which a swarm of agents tries to improve solutions from the population of solutions
Summary
Pioneering work in the area of the population-based metaheuristics included genetic algorithms (GA) [1], evolutionary computations—EC [2], particle swarm optimization (PSO) [3] and its two offspring—ant colony optimization [4] and bee colony algorithms (BCA) [5]. Since the end of the last century, tremendous research effort has brought a variety of approaches and population-based metaheuristics. In 2013 Boussai et al [6] proposed the classification of the population-based metaheuristics and grouped them into two broad classes. The second class is swarm intelligence with subclasses, including ant colony optimization, particle swarm optimization, bacterial foraging optimization, bee colony optimization, artificial immune systems and biogeography-based optimization. Over the last 20 years, more than a few thousand new population-based approaches and many new subclasses have been proposed. A recent survey of the population-based techniques can be found in [7]
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