Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array
This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis.Precisely, the MCS algorithm is proposed by incorporating...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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2017
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| Online Access: | https://repo.uum.edu.my/id/eprint/24280/1/SR%207%2046521%20%282017%29%201%2019.pdf |
| _version_ | 1803628692159594496 |
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| author | Abdul Rani, Khairul Najmi Abdulmalek, Mohamed Fareq Rahim, Hasliza Siew Chin, Neoh Abd Wahab, Alawiyah |
| author_facet | Abdul Rani, Khairul Najmi Abdulmalek, Mohamed Fareq Rahim, Hasliza Siew Chin, Neoh Abd Wahab, Alawiyah |
| author_sort | Abdul Rani, Khairul Najmi |
| collection | UUM |
| description | This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis.Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best
host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively.All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler–Deb–Thiele’s (ZDT’s) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously. |
| first_indexed | 2024-07-04T06:26:00Z |
| format | Article |
| id | uum-24280 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:26:00Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | uum-242802018-06-25T01:09:20Z https://repo.uum.edu.my/id/eprint/24280/ Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array Abdul Rani, Khairul Najmi Abdulmalek, Mohamed Fareq Rahim, Hasliza Siew Chin, Neoh Abd Wahab, Alawiyah QA75 Electronic computers. Computer science This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis.Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively.All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler–Deb–Thiele’s (ZDT’s) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously. 2017 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/24280/1/SR%207%2046521%20%282017%29%201%2019.pdf Abdul Rani, Khairul Najmi and Abdulmalek, Mohamed Fareq and Rahim, Hasliza and Siew Chin, Neoh and Abd Wahab, Alawiyah (2017) Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array. Scientific Reports, 7. pp. 1-19. ISSN 2045-2322 http://doi.org/10.1038/srep46521 doi:10.1038/srep46521 doi:10.1038/srep46521 |
| spellingShingle | QA75 Electronic computers. Computer science Abdul Rani, Khairul Najmi Abdulmalek, Mohamed Fareq Rahim, Hasliza Siew Chin, Neoh Abd Wahab, Alawiyah Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array |
| title | Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array |
| title_full | Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array |
| title_fullStr | Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array |
| title_full_unstemmed | Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array |
| title_short | Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array |
| title_sort | hybridization of strength pareto multiobjective optimization with modified cuckoo search algorithm for rectangular array |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/24280/1/SR%207%2046521%20%282017%29%201%2019.pdf |
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