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Title
Abstract
Figures
Introduction
Experimental Setup
Performance Analysis of Proposed Algorithms
Discussion
Conclusion
References
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Computer Science
Algorithms and Data Structures

Multi-Modal Bat Algorithm with Improved Search (MMBAIS)

Profile image of Reshu ChaudharyReshu Chaudhary

2017, Journal of Computational Science

https://doi.org/10.1016/J.JOCS.2016.12.003
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18 pages

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Abstract

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.

Figures (11)
where a and y are pre-defined constants, and r°° is the initial pulse emission rate of i™ bat. 3. Multi-Modal Bat Algorithm with Improved Search (MMBAIS) Bats, given in any ecological environment, hunt for their prey using echolocation. Energy spent during flight (i.e. in the flapping of wings), combined with ultrasonic echolocation is high. Usually there are multiple prey present at a time, and larger prey being a source of higher energy are more preferable. But larger prey might be few or at a greater distance, as compared to smaller prey. Hence, selection of prey for bats, and other foraging animals, involves a trade-off between the potential energy gain from the prey, and energy spent in catching it. Therefore, bats are more likely to select prey that has the maximum potential to provide with the highest net energy intake [24][25]. That is, given the high cost of flight and search, the time spent looking for prey should be minimised and hence places that provide bats with maximum energy gain lead to aggregation of bats towards these regions [26][27].
where a and y are pre-defined constants, and r°° is the initial pulse emission rate of i™ bat. 3. Multi-Modal Bat Algorithm with Improved Search (MMBAIS) Bats, given in any ecological environment, hunt for their prey using echolocation. Energy spent during flight (i.e. in the flapping of wings), combined with ultrasonic echolocation is high. Usually there are multiple prey present at a time, and larger prey being a source of higher energy are more preferable. But larger prey might be few or at a greater distance, as compared to smaller prey. Hence, selection of prey for bats, and other foraging animals, involves a trade-off between the potential energy gain from the prey, and energy spent in catching it. Therefore, bats are more likely to select prey that has the maximum potential to provide with the highest net energy intake [24][25]. That is, given the high cost of flight and search, the time spent looking for prey should be minimised and hence places that provide bats with maximum energy gain lead to aggregation of bats towards these regions [26][27].
fin: known global best, C: modality, EM : Error Margin, f -1.801 for d=2, -4.687 for d=5 and -9.66 for d=10 Table 1: List of Numerical Optimisation Functions used
fin: known global best, C: modality, EM : Error Margin, f -1.801 for d=2, -4.687 for d=5 and -9.66 for d=10 Table 1: List of Numerical Optimisation Functions used
5. Performance Analysis of Proposed Algorithms 5.1 Comparison with BA SS EEE EEE EEE To have a fair comparison, the average of results generated over 100 runs is considered. Comparison of MMBAIS MMBA and BAIS with BA for efficacy [31] is done over all 30 functions listed in Table 1. Parameter values mentione in Table 2 have been used. The results are presented in Table 7. The minimum, mean and maximum difference betwee the known optimal value and the computed best values, i.e. the error/deviation from the known best over 100 iteration are reported. To determine that the mean objective values are significantly improved, student’s t-test is also employec with confidence level of 95% [15].
5. Performance Analysis of Proposed Algorithms 5.1 Comparison with BA SS EEE EEE EEE To have a fair comparison, the average of results generated over 100 runs is considered. Comparison of MMBAIS MMBA and BAIS with BA for efficacy [31] is done over all 30 functions listed in Table 1. Parameter values mentione in Table 2 have been used. The results are presented in Table 7. The minimum, mean and maximum difference betwee the known optimal value and the computed best values, i.e. the error/deviation from the known best over 100 iteration are reported. To determine that the mean objective values are significantly improved, student’s t-test is also employec with confidence level of 95% [15].
? igure 3(continued): Convergence comparison of BA, MMBA, BAILS, MMBAILS and CBA over benchmark functions (Table 1).
? igure 3(continued): Convergence comparison of BA, MMBA, BAILS, MMBAILS and CBA over benchmark functions (Table 1).
a I‘ RE Reshu Chaudhary is pursuing her PhD in Computer Science from the Department of Computer Science, University of Delhi, India, under the guidance of Dr. Hema Banati. She completed her Masters in Computer A pplications (MCA) from Department of Computer Science, and Bachelor of Science from Hansraj College, both under University of Delhi, India. Her areas of research include soft computing and big data. Dr Hema Banati completed her PhD after her Masters in Computer A pplications (M.C.A) both from Department of Computer Science, University of Delhi, India. At present she is an Associate Professor in the Department of Computer Science, Dyal Singh College, University of Delhi. She has over 20 years of teaching experience to both undergraduate and postgraduate classes. Her research publications span across various reputed national, international journals, conferences and books. Her areas of interest include e-learning, e-commerce, soft computing, social networks and big data.
a I‘ RE Reshu Chaudhary is pursuing her PhD in Computer Science from the Department of Computer Science, University of Delhi, India, under the guidance of Dr. Hema Banati. She completed her Masters in Computer A pplications (MCA) from Department of Computer Science, and Bachelor of Science from Hansraj College, both under University of Delhi, India. Her areas of research include soft computing and big data. Dr Hema Banati completed her PhD after her Masters in Computer A pplications (M.C.A) both from Department of Computer Science, University of Delhi, India. At present she is an Associate Professor in the Department of Computer Science, Dyal Singh College, University of Delhi. She has over 20 years of teaching experience to both undergraduate and postgraduate classes. Her research publications span across various reputed national, international journals, conferences and books. Her areas of interest include e-learning, e-commerce, soft computing, social networks and big data.

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