A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems
Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly cat...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5521951 |
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| _version_ | 1832549947382169600 |
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| author | Wali Khan Mashwani Ruqayya Haider Samir Brahim Belhaouari |
| author_facet | Wali Khan Mashwani Ruqayya Haider Samir Brahim Belhaouari |
| author_sort | Wali Khan Mashwani |
| collection | DOAJ |
| description | Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems. |
| format | Article |
| id | doaj-art-8282b18a651b48a380e59ca178a31179 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8282b18a651b48a380e59ca178a311792025-02-03T06:08:08ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55219515521951A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization ProblemsWali Khan Mashwani0Ruqayya Haider1Samir Brahim Belhaouari2Institute of Numerical Sciences, Kohat University of Science & Technology (KUST) Technology, Kohat 26000, Khyber PakhtunKhwa (KPK), PakistanInstitute of Numerical Sciences, Kohat University of Science & Technology (KUST) Technology, Kohat 26000, Khyber PakhtunKhwa (KPK), PakistanDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Education City, Qatar Foundation, Doha, QatarConstrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.http://dx.doi.org/10.1155/2021/5521951 |
| spellingShingle | Wali Khan Mashwani Ruqayya Haider Samir Brahim Belhaouari A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems Complexity |
| title | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_full | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_fullStr | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_full_unstemmed | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_short | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_sort | multiswarm intelligence algorithm for expensive bound constrained optimization problems |
| url | http://dx.doi.org/10.1155/2021/5521951 |
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