Biology migration algorithm: a new nature-inspired heuristic methodology for global optimization
Author(s)
Zhang, Qingyang
Wang, Ronggui
Yang, Juan
Lewis, Andrew
Chiclana, Francisco
Yang, Shengxiang
Griffith University Author(s)
Year published
2019
Metadata
Show full item recordAbstract
In this paper, inspired by the biology migration phenomenon, which is ubiquitous in the social evolution process in nature, a new meta-heuristic optimization paradigm called biology migration algorithm (BMA) is proposed. This optimizer consists of two phases, i.e., migration phase and updating phase. The first phase mainly simulates how the species move to new habits. During this phase, each agent should obey two main rules depicted by two random operators. The second phase mimics how some species leave the group and new ones join the group during the migration process. In this phase, a maximum number of iterations will be ...
View more >In this paper, inspired by the biology migration phenomenon, which is ubiquitous in the social evolution process in nature, a new meta-heuristic optimization paradigm called biology migration algorithm (BMA) is proposed. This optimizer consists of two phases, i.e., migration phase and updating phase. The first phase mainly simulates how the species move to new habits. During this phase, each agent should obey two main rules depicted by two random operators. The second phase mimics how some species leave the group and new ones join the group during the migration process. In this phase, a maximum number of iterations will be set to predetermine whether a current individual should leave and be replaced by a new one. Simulation results based on a comprehensive set of benchmark functions and four real engineering problems indicate that BMA is effective in comparison with other existing optimization methods.
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View more >In this paper, inspired by the biology migration phenomenon, which is ubiquitous in the social evolution process in nature, a new meta-heuristic optimization paradigm called biology migration algorithm (BMA) is proposed. This optimizer consists of two phases, i.e., migration phase and updating phase. The first phase mainly simulates how the species move to new habits. During this phase, each agent should obey two main rules depicted by two random operators. The second phase mimics how some species leave the group and new ones join the group during the migration process. In this phase, a maximum number of iterations will be set to predetermine whether a current individual should leave and be replaced by a new one. Simulation results based on a comprehensive set of benchmark functions and four real engineering problems indicate that BMA is effective in comparison with other existing optimization methods.
View less >
Journal Title
Soft Computing
Volume
23
Issue
16
Subject
Applied Mathematics
Artificial Intelligence and Image Processing
Cognitive Sciences