A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space
Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic...
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| Format: | Article |
| Language: | English |
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Ram Arti Publishers
2019-04-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
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| Online Access: | https://www.ijmems.in/assets//39-ijmems-18-299_vol.-4%2c-no.-2%2c-471%E2%80%93488%2c-2019.pdf |
| _version_ | 1811275660291735552 |
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| author | Brahm Prakash Dahiya Shaveta Rani Paramjeet Singh |
| author_facet | Brahm Prakash Dahiya Shaveta Rani Paramjeet Singh |
| author_sort | Brahm Prakash Dahiya |
| collection | DOAJ |
| description | Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimization (HAGOA) meta-heuristic algorithm is proposed to improve the exploitation and exploration in given search space. HAGOA is inherited Salp swarm behaviors. HAGOA performs balancing in exploitation and exploration search space. It is capable to make chain system between exploitation and exploration phases. The efficiency of HAGOA meta-heuristic algorithm will analyze using 19 benchmarks functions from F1 to F19. In this paper, HAGOA algorithm is performed efficiency analyze test with Artificial Grasshopper optimization (AGOA), Hybrid Artificial Bee Colony with Salp (HABCS), Modified Artificial Bee Colony (MABC), and Modify Particle Swarm Optimization (MPSO) swarm based meta-heuristic algorithms using uni-modal and multi-modal functions in MATLAB. Comparison results are shown that HAGOA meta-heuristic algorithm is performed better efficiency than other swarm intelligence algorithms on the basics of high exploitation, high exploration, and high convergence rate. It also performed perfect balancing between exploitation and exploration in given search space. |
| first_indexed | 2024-04-12T23:42:52Z |
| format | Article |
| id | doaj.art-aa197c5c144f47438f58ea67a0d4e865 |
| institution | Directory Open Access Journal |
| issn | 2455-7749 2455-7749 |
| language | English |
| last_indexed | 2024-04-12T23:42:52Z |
| publishDate | 2019-04-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj.art-aa197c5c144f47438f58ea67a0d4e8652022-12-22T03:11:57ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492455-77492019-04-014247148810.33889/IJMEMS.2019.4.2-039A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search SpaceBrahm Prakash Dahiya0Shaveta Rani1Paramjeet Singh2Department of Computer Science and Engineering , I. K. G. Punjab Technical University, Punjab, IndiaDepartment of Computer Science and Engineering , Giani Zail Singh Campus College of Engineering and Technology, Punjab, IndiaDepartment of Computer Science and Engineering , Giani Zail Singh Campus College of Engineering and Technology, Punjab, IndiaMeta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimization (HAGOA) meta-heuristic algorithm is proposed to improve the exploitation and exploration in given search space. HAGOA is inherited Salp swarm behaviors. HAGOA performs balancing in exploitation and exploration search space. It is capable to make chain system between exploitation and exploration phases. The efficiency of HAGOA meta-heuristic algorithm will analyze using 19 benchmarks functions from F1 to F19. In this paper, HAGOA algorithm is performed efficiency analyze test with Artificial Grasshopper optimization (AGOA), Hybrid Artificial Bee Colony with Salp (HABCS), Modified Artificial Bee Colony (MABC), and Modify Particle Swarm Optimization (MPSO) swarm based meta-heuristic algorithms using uni-modal and multi-modal functions in MATLAB. Comparison results are shown that HAGOA meta-heuristic algorithm is performed better efficiency than other swarm intelligence algorithms on the basics of high exploitation, high exploration, and high convergence rate. It also performed perfect balancing between exploitation and exploration in given search space.https://www.ijmems.in/assets//39-ijmems-18-299_vol.-4%2c-no.-2%2c-471%E2%80%93488%2c-2019.pdfSwarm intelligence (SI)Hybrid artificial grasshopper optimization (HAGOA)Modified artificial bee colony (MABC)Modify particle swarm optimization (MPSO) |
| spellingShingle | Brahm Prakash Dahiya Shaveta Rani Paramjeet Singh A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space International Journal of Mathematical, Engineering and Management Sciences Swarm intelligence (SI) Hybrid artificial grasshopper optimization (HAGOA) Modified artificial bee colony (MABC) Modify particle swarm optimization (MPSO) |
| title | A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space |
| title_full | A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space |
| title_fullStr | A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space |
| title_full_unstemmed | A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space |
| title_short | A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space |
| title_sort | hybrid artificial grasshopper optimization hagoa meta heuristic approach a hybrid optimizer for discover the global optimum in given search space |
| topic | Swarm intelligence (SI) Hybrid artificial grasshopper optimization (HAGOA) Modified artificial bee colony (MABC) Modify particle swarm optimization (MPSO) |
| url | https://www.ijmems.in/assets//39-ijmems-18-299_vol.-4%2c-no.-2%2c-471%E2%80%93488%2c-2019.pdf |
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