Selective opposition based constrained barnacle mating optimization: Theory and applications
Mathematical models of Barnacle Mating Optimization (BMO) are based on observations of real-world barnacle mating behaviors such as sperm casting and self-fertilization. Nevertheless, BMO considers penis length to produce new offspring through pseudo-copulated mating behavior, with no constraints li...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Control and Optimization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720724001176 |
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| author | Marzia Ahmed Mohd Herwan Sulaiman Md. Maruf Hassan Md. Atikur Rahaman Masuk Abdullah |
| author_facet | Marzia Ahmed Mohd Herwan Sulaiman Md. Maruf Hassan Md. Atikur Rahaman Masuk Abdullah |
| author_sort | Marzia Ahmed |
| collection | DOAJ |
| description | Mathematical models of Barnacle Mating Optimization (BMO) are based on observations of real-world barnacle mating behaviors such as sperm casting and self-fertilization. Nevertheless, BMO considers penis length to produce new offspring through pseudo-copulated mating behavior, with no constraints like strong wave motion, food availability, or wind direction considered. Exploration and exploitation are two crucial optimization stages as we implement the constrained BMO. They are informed by models of navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement experienced by barnacles during mating. We will later integrate opposition-based learning (OBL) with constrained BMO (C-BMO) to improve its exploratory behavior while retaining a quick convergence rate. Rather than opposing all barnacle dimensions, we just opposed those that went over the border. In addition to increasing efficiency by cutting down on wasted time spent exploring, this also increases the likelihood of stumbling onto optimal solutions. After that, it is put through its paces in a real-world case study, where it proves to be superior to the most cutting-edge algorithms available. |
| format | Article |
| id | doaj-art-c2c52dd900c24dbe8bcd16f693fb7b20 |
| institution | Kabale University |
| issn | 2666-7207 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj-art-c2c52dd900c24dbe8bcd16f693fb7b202024-12-17T05:01:19ZengElsevierResults in Control and Optimization2666-72072024-12-0117100487Selective opposition based constrained barnacle mating optimization: Theory and applicationsMarzia Ahmed0Mohd Herwan Sulaiman1Md. Maruf Hassan2Md. Atikur Rahaman3Masuk Abdullah4Department of Software Engineering, Daffodil International University, Daffodil Smart City, Ashulia, 1341, Dhaka, BangladeshUniversiti Malaysia Pahang Al-sultan Abdullah, Pekan, 26600, Pahang, MalaysiaDepartment of Software Engineering, Daffodil International University, Daffodil Smart City, Ashulia, 1341, Dhaka, BangladeshSchool of Economics and Management, Jiujiang University, 551 Qianjin Donglu, Jiujiang, Jiangxi 332005, PR ChinaFaculty of Engineering, University of Debrecen, street. 2-4, Ótemető, 4028, Debrecen, Hungary; Corresponding author.Mathematical models of Barnacle Mating Optimization (BMO) are based on observations of real-world barnacle mating behaviors such as sperm casting and self-fertilization. Nevertheless, BMO considers penis length to produce new offspring through pseudo-copulated mating behavior, with no constraints like strong wave motion, food availability, or wind direction considered. Exploration and exploitation are two crucial optimization stages as we implement the constrained BMO. They are informed by models of navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement experienced by barnacles during mating. We will later integrate opposition-based learning (OBL) with constrained BMO (C-BMO) to improve its exploratory behavior while retaining a quick convergence rate. Rather than opposing all barnacle dimensions, we just opposed those that went over the border. In addition to increasing efficiency by cutting down on wasted time spent exploring, this also increases the likelihood of stumbling onto optimal solutions. After that, it is put through its paces in a real-world case study, where it proves to be superior to the most cutting-edge algorithms available.http://www.sciencedirect.com/science/article/pii/S2666720724001176Barnacle mating optimizerConstrained optimizationOpposition-based learningSelective oppositionMachine learningTime-series prediction |
| spellingShingle | Marzia Ahmed Mohd Herwan Sulaiman Md. Maruf Hassan Md. Atikur Rahaman Masuk Abdullah Selective opposition based constrained barnacle mating optimization: Theory and applications Results in Control and Optimization Barnacle mating optimizer Constrained optimization Opposition-based learning Selective opposition Machine learning Time-series prediction |
| title | Selective opposition based constrained barnacle mating optimization: Theory and applications |
| title_full | Selective opposition based constrained barnacle mating optimization: Theory and applications |
| title_fullStr | Selective opposition based constrained barnacle mating optimization: Theory and applications |
| title_full_unstemmed | Selective opposition based constrained barnacle mating optimization: Theory and applications |
| title_short | Selective opposition based constrained barnacle mating optimization: Theory and applications |
| title_sort | selective opposition based constrained barnacle mating optimization theory and applications |
| topic | Barnacle mating optimizer Constrained optimization Opposition-based learning Selective opposition Machine learning Time-series prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2666720724001176 |
| work_keys_str_mv | AT marziaahmed selectiveoppositionbasedconstrainedbarnaclematingoptimizationtheoryandapplications AT mohdherwansulaiman selectiveoppositionbasedconstrainedbarnaclematingoptimizationtheoryandapplications AT mdmarufhassan selectiveoppositionbasedconstrainedbarnaclematingoptimizationtheoryandapplications AT mdatikurrahaman selectiveoppositionbasedconstrainedbarnaclematingoptimizationtheoryandapplications AT masukabdullah selectiveoppositionbasedconstrainedbarnaclematingoptimizationtheoryandapplications |