Decomposition Based Quantum Inspired Salp Swarm Algorithm for Multiobjective Optimization
Various real-world problems are essentially multiobjective optimization problems (MOPs), which involve several conflicting objectives. We propose a Quantum-inspired multiobjective Salp Swarm Algorithm based on the Decomposition technique to locate the multiple Pareto-optimal solutions (POS). The mai...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9903587/ |
| _version_ | 1811239276291031040 |
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| author | Sanjai Pathak Ashish Mani Mayank Sharma Amlan Chatterjee |
| author_facet | Sanjai Pathak Ashish Mani Mayank Sharma Amlan Chatterjee |
| author_sort | Sanjai Pathak |
| collection | DOAJ |
| description | Various real-world problems are essentially multiobjective optimization problems (MOPs), which involve several conflicting objectives. We propose a Quantum-inspired multiobjective Salp Swarm Algorithm based on the Decomposition technique to locate the multiple Pareto-optimal solutions (POS). The main objective while designing the algorithms for the multiobjective optimization problems is to attain a good convergence and uniform dissemination of the solutions, which remains a significant challenge for the algorithms. The proposed Decomposition-based Quantum-inspired Salp Swarm Algorithm for Multiobjective Optimization (DMQSSA) extends the primary form of SSA by using the quantum-inspired framework and a basic decomposition strategy to improve the balance between exploration and exploitation for MOPs. The Delta potential-well model (DPWM) from quantum mechanics is known for enhancing the convergence and diversity in the population, and the decomposition strategy is proved to be effective to generate evenly distributed solutions set on the Pareto front for simultaneous optimization of the subproblems. In this paper, the existing DPWM model is analysed and redesigned for MOPs with a modification in the contraction equation, and decomposition strategy is used along with an intelligent selection technique to ensure non-dominated solutions. The proposed hybrid approach is evaluated and compared with other techniques on a set of well-known benchmark problems. The results show that DMQSSA can handle the multiobjective optimization problems to find better and well-distributed Pareto optimal set. Also, success of the proposed algorithm is further illustrated on a real-world application. |
| first_indexed | 2024-04-12T12:56:54Z |
| format | Article |
| id | doaj.art-7343c7e48c004a8aba28a5c3b32e5aa2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T12:56:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7343c7e48c004a8aba28a5c3b32e5aa22022-12-22T03:32:18ZengIEEEIEEE Access2169-35362022-01-011010542110543610.1109/ACCESS.2022.32101359903587Decomposition Based Quantum Inspired Salp Swarm Algorithm for Multiobjective OptimizationSanjai Pathak0https://orcid.org/0000-0003-2253-4591Ashish Mani1https://orcid.org/0000-0002-2312-1185Mayank Sharma2Amlan Chatterjee3Amity University, Noida, Uttar Pradesh, IndiaAmity University, Noida, Uttar Pradesh, IndiaAmity University, Noida, Uttar Pradesh, IndiaCalifornia State University, Dominguez Hills, Carson, CA, USAVarious real-world problems are essentially multiobjective optimization problems (MOPs), which involve several conflicting objectives. We propose a Quantum-inspired multiobjective Salp Swarm Algorithm based on the Decomposition technique to locate the multiple Pareto-optimal solutions (POS). The main objective while designing the algorithms for the multiobjective optimization problems is to attain a good convergence and uniform dissemination of the solutions, which remains a significant challenge for the algorithms. The proposed Decomposition-based Quantum-inspired Salp Swarm Algorithm for Multiobjective Optimization (DMQSSA) extends the primary form of SSA by using the quantum-inspired framework and a basic decomposition strategy to improve the balance between exploration and exploitation for MOPs. The Delta potential-well model (DPWM) from quantum mechanics is known for enhancing the convergence and diversity in the population, and the decomposition strategy is proved to be effective to generate evenly distributed solutions set on the Pareto front for simultaneous optimization of the subproblems. In this paper, the existing DPWM model is analysed and redesigned for MOPs with a modification in the contraction equation, and decomposition strategy is used along with an intelligent selection technique to ensure non-dominated solutions. The proposed hybrid approach is evaluated and compared with other techniques on a set of well-known benchmark problems. The results show that DMQSSA can handle the multiobjective optimization problems to find better and well-distributed Pareto optimal set. Also, success of the proposed algorithm is further illustrated on a real-world application.https://ieeexplore.ieee.org/document/9903587/Swarm intelligencesalp swarm algorithmmultiobjective optimizationthe controller placement problem |
| spellingShingle | Sanjai Pathak Ashish Mani Mayank Sharma Amlan Chatterjee Decomposition Based Quantum Inspired Salp Swarm Algorithm for Multiobjective Optimization IEEE Access Swarm intelligence salp swarm algorithm multiobjective optimization the controller placement problem |
| title | Decomposition Based Quantum Inspired Salp Swarm Algorithm for Multiobjective Optimization |
| title_full | Decomposition Based Quantum Inspired Salp Swarm Algorithm for Multiobjective Optimization |
| title_fullStr | Decomposition Based Quantum Inspired Salp Swarm Algorithm for Multiobjective Optimization |
| title_full_unstemmed | Decomposition Based Quantum Inspired Salp Swarm Algorithm for Multiobjective Optimization |
| title_short | Decomposition Based Quantum Inspired Salp Swarm Algorithm for Multiobjective Optimization |
| title_sort | decomposition based quantum inspired salp swarm algorithm for multiobjective optimization |
| topic | Swarm intelligence salp swarm algorithm multiobjective optimization the controller placement problem |
| url | https://ieeexplore.ieee.org/document/9903587/ |
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