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Multi-Modal Bat Algorithm with Improved Search (MMBAIS)
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DOI：10.1016/j.jocs.2016.12.003.png)
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Abstract
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Bat Algorithm (BA), is a relatively new nature inspired metaheuristic algorithm, which works on the echolocation capabilities of micro-bats. Although being highly efficient, it suffers from pre-mature convergence. To overcome this limitation, this paper proposes a multimodal variant of BA, called Multi-Modal Bat Algorithm (MMBA), which includes the foraging behaviour of bats. The standard BA exhibits a random movement for catching its prey. This work also proposes an enhancement to these exploration capabilities of bat, called Bat Algorithm with Improved Search (BAIS). Each of these variants is tested for its efficacy against BA over 30 benchmark functions. An integration of both these modifications, the Multi-Modal Bat Algorithm with Improved Search (MMBAIS), is also subsequently compared against the same 30 benchmark functions. Results established the superiority of MMBAIS over BA. Experimental comparison of MMBAIS with a recent variant of BA also revealed the efficiency of MMBAIS. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Bat algorithm
Multi-modal algorithm
Swarm intelligence
Numerical optimisation
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