Coordinate Exhaustive Search Hybridization Enhancing Evolutionary Optimization Algorithms
In general, all of the hybridized evolutionary optimization algorithms use “first diversification and then intensification” routine approach. In other words, these hybridized methods all begin with a global search mode using a highly random initial search population and then switch to intense local...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2020-07-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
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| Online Access: | http://jad.shahroodut.ac.ir/article_1612_0f1ab56c8f1415583fc32a8fee40e40b.pdf |
| _version_ | 1818383456856440832 |
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| author | Osman K. Erol I. Eksin A. Akdemir A. Aydınoglu |
| author_facet | Osman K. Erol I. Eksin A. Akdemir A. Aydınoglu |
| author_sort | Osman K. Erol |
| collection | DOAJ |
| description | In general, all of the hybridized evolutionary optimization algorithms use “first diversification and then intensification” routine approach. In other words, these hybridized methods all begin with a global search mode using a highly random initial search population and then switch to intense local search mode at some stage. The population initialization is still a crucial point in the hybridized evolutionary optimization algorithms since it can affect the speed of convergence and the quality of the final solution. In this study, we introduce a new approach by creating a paradigm shift that reverses the “diversification” and then “intensification” routines. Here, instead of starting from a random initial population, we firstly find a unique starting point by conducting an initial exhaustive search based on the coordinate exhaustive search local optimization algorithm only for single step iteration in order to collect a rough but some meaningful knowledge about the nature of the problem. Thus, our main assertion is that this approach will ameliorate convergence rate of any evolutionary optimization algorithms. In this study, we illustrate how one can use this unique starting point in the initialization of two evolutionary optimization algorithms, including but not limited to Big Bang-Big Crunch optimization and Particle Swarm Optimization. Experiments on a commonly used benchmark test suite, which consist of mainly rotated and shifted functions, show that the proposed initialization procedure leads to great improvement for the above-mentioned two evolutionary optimization algorithms. |
| first_indexed | 2024-12-14T03:06:40Z |
| format | Article |
| id | doaj.art-547bf7313db64ca6a5c2ac75d7610af6 |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-14T03:06:40Z |
| publishDate | 2020-07-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-547bf7313db64ca6a5c2ac75d7610af62022-12-21T23:19:23ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442020-07-018343944910.22044/jadm.2019.7351.18751612Coordinate Exhaustive Search Hybridization Enhancing Evolutionary Optimization AlgorithmsOsman K. Erol0I. Eksin1A. Akdemir2A. Aydınoglu3Istanbul Technical University, Electric-Electronics Faculty, Control and Automation Dept., Maslak, Sariyer, Turkey.Istanbul Technical University, Electric-Electronics Faculty, Control and Automation Dept., Maslak, Sariyer, Turkey.Bogazici University, Engineering Faculty, Computer Engineering Dept., Bebek, Besiktas, Turkey.Istanbul Technical University, Electric-Electronics Faculty, Control and Automation Dept., Maslak, Sariyer, Turkey.In general, all of the hybridized evolutionary optimization algorithms use “first diversification and then intensification” routine approach. In other words, these hybridized methods all begin with a global search mode using a highly random initial search population and then switch to intense local search mode at some stage. The population initialization is still a crucial point in the hybridized evolutionary optimization algorithms since it can affect the speed of convergence and the quality of the final solution. In this study, we introduce a new approach by creating a paradigm shift that reverses the “diversification” and then “intensification” routines. Here, instead of starting from a random initial population, we firstly find a unique starting point by conducting an initial exhaustive search based on the coordinate exhaustive search local optimization algorithm only for single step iteration in order to collect a rough but some meaningful knowledge about the nature of the problem. Thus, our main assertion is that this approach will ameliorate convergence rate of any evolutionary optimization algorithms. In this study, we illustrate how one can use this unique starting point in the initialization of two evolutionary optimization algorithms, including but not limited to Big Bang-Big Crunch optimization and Particle Swarm Optimization. Experiments on a commonly used benchmark test suite, which consist of mainly rotated and shifted functions, show that the proposed initialization procedure leads to great improvement for the above-mentioned two evolutionary optimization algorithms.http://jad.shahroodut.ac.ir/article_1612_0f1ab56c8f1415583fc32a8fee40e40b.pdfcoordinate exhaustive searchevolutionary computationbig bang- big crunch optimization algorithmhybridizationa-priori knowledge utilization |
| spellingShingle | Osman K. Erol I. Eksin A. Akdemir A. Aydınoglu Coordinate Exhaustive Search Hybridization Enhancing Evolutionary Optimization Algorithms Journal of Artificial Intelligence and Data Mining coordinate exhaustive search evolutionary computation big bang- big crunch optimization algorithm hybridization a-priori knowledge utilization |
| title | Coordinate Exhaustive Search Hybridization Enhancing Evolutionary Optimization Algorithms |
| title_full | Coordinate Exhaustive Search Hybridization Enhancing Evolutionary Optimization Algorithms |
| title_fullStr | Coordinate Exhaustive Search Hybridization Enhancing Evolutionary Optimization Algorithms |
| title_full_unstemmed | Coordinate Exhaustive Search Hybridization Enhancing Evolutionary Optimization Algorithms |
| title_short | Coordinate Exhaustive Search Hybridization Enhancing Evolutionary Optimization Algorithms |
| title_sort | coordinate exhaustive search hybridization enhancing evolutionary optimization algorithms |
| topic | coordinate exhaustive search evolutionary computation big bang- big crunch optimization algorithm hybridization a-priori knowledge utilization |
| url | http://jad.shahroodut.ac.ir/article_1612_0f1ab56c8f1415583fc32a8fee40e40b.pdf |
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