Parallel multi-agent real-coded genetic algorithm for large-scale black-box single-objective optimisation

There is an ongoing evolution involving a new approach to large-scale optimisations based on co-evolutionary searches using interacting heterogeneous agent-processes via the implementation of synchronised genetic algorithms with local populations. The individualisation of heuristic operators at the...

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Veröffentlicht in:Knowledge-based systems Jg. 174; S. 103 - 122
Hauptverfasser: Akopov, Andranik S., Beklaryan, Levon A., Thakur, Manoj, Verma, Bhisham Dev
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 15.06.2019
Elsevier Science Ltd
Schlagworte:
Agents
Algorithms
Biological evolution
Black-box single-objective optimisation
Computer simulation
Crossovers
Economic models
Genetic algorithms
Genetics
Heuristic
Multi-agent genetic algorithms
Multiagent systems
Objectives
Operators
Optimization
Process parameters
Real-coded genetic algorithms
Searching
Simulation
Simulation modelling
World problems
Multi-agent genetic algorithms
Real-coded genetic algorithms
Black-box single-objective optimisation
Simulation modelling
ISSN:0950-7051, 1872-7409
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Zusammenfassung:There is an ongoing evolution involving a new approach to large-scale optimisations based on co-evolutionary searches using interacting heterogeneous agent-processes via the implementation of synchronised genetic algorithms with local populations. The individualisation of heuristic operators at the level of agent-processes that implement independent evolutionary searches facilitate the improved likelihood of obtaining the best solutions in the fastest time. Based on this property, a parallel multi-agent single-objective real-coded genetic algorithm for large-scale constrained black-box single-objective optimisations (LSOPs) is proposed. This facilitates the effective frequency exchange of the best potential decisions between interacting agent-processes with individual parameters, such as types of crossover and mutation operators with their own characteristics. We have improved the quality of both solutions and the time-efficiency of a multi-agent real-coded genetic algorithm (MA−RCGA). A novel framework was developed that represents the aggregation of MA−RCGA with simulation models by implementing a set of objective functions for real-world large-scale optimisation problems such as the simulation model of the ecological-economics system implemented in the AnyLogic tool. •We developed a new multi-agent real-coded genetic algorithm (MA-RCGA).•There are investigated different performance characteristics of MA-RCGA.•MA-RCGA has been compared with other optimisation algorithms using test instances.•MA-RCGA was applied for solving a large-scale single-objective optimisation problem.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.03.003