Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization Problem
This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail to satisfy constraints while achieving optimal pe...
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MDPI AG
2023-04-01
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author | Muniyan Rajeswari Rajakumar Ramalingam Shakila Basheer Keerthi Samhitha Babu Mamoon Rashid Ramar Saranya |
author_facet | Muniyan Rajeswari Rajakumar Ramalingam Shakila Basheer Keerthi Samhitha Babu Mamoon Rashid Ramar Saranya |
author_sort | Muniyan Rajeswari |
collection | DOAJ |
description | This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail to satisfy constraints while achieving optimal performance. To address this issue, we propose a multi-objective artificial bee colony optimization algorithm with a classical multi-objective theme called fitness sharing. This approach helps the convergence of the Pareto solution set towards a single optimal solution that satisfies multiple objectives. This article introduces multi-objective optimization with an example of a non-dominated sequencing technique and fitness sharing approach. The experimentation is carried out in MATLAB 2018a. In addition, we applied the proposed algorithm to two different real-time datasets, namely the knapsack problem and the nurse scheduling problem (NSP). The outcome of the proposed MBABC-NM algorithm is evaluated using standard performance indicators such as average distance, number of reference solutions (NRS), overall count of attained solutions (TNS), and overall non-dominated generation volume (ONGV). The results show that it outperforms other algorithms. |
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issn | 2075-1680 |
language | English |
last_indexed | 2024-03-11T05:14:46Z |
publishDate | 2023-04-01 |
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spelling | doaj.art-3a5070d720bf4ba4ae7e69f9eba93faa2023-11-17T18:19:45ZengMDPI AGAxioms2075-16802023-04-0112439510.3390/axioms12040395Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization ProblemMuniyan Rajeswari0Rajakumar Ramalingam1Shakila Basheer2Keerthi Samhitha Babu3Mamoon Rashid4Ramar Saranya5Department of Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Pondicherry 605107, IndiaDepartment of Computer Science and Technology, Madanapalle Institute of Technology and Science, Madanapalle 517325, IndiaDepartment of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science and Information Technology, KL Deemed to be University, Guntur District, Vaddeswaram 522302, IndiaDepartment of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, IndiaDepartment of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli 627012, IndiaThis article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail to satisfy constraints while achieving optimal performance. To address this issue, we propose a multi-objective artificial bee colony optimization algorithm with a classical multi-objective theme called fitness sharing. This approach helps the convergence of the Pareto solution set towards a single optimal solution that satisfies multiple objectives. This article introduces multi-objective optimization with an example of a non-dominated sequencing technique and fitness sharing approach. The experimentation is carried out in MATLAB 2018a. In addition, we applied the proposed algorithm to two different real-time datasets, namely the knapsack problem and the nurse scheduling problem (NSP). The outcome of the proposed MBABC-NM algorithm is evaluated using standard performance indicators such as average distance, number of reference solutions (NRS), overall count of attained solutions (TNS), and overall non-dominated generation volume (ONGV). The results show that it outperforms other algorithms.https://www.mdpi.com/2075-1680/12/4/395artificial bee colonyNelder—Meadmulti-objective optimization0-1 knapsack problemnurse scheduling problem |
spellingShingle | Muniyan Rajeswari Rajakumar Ramalingam Shakila Basheer Keerthi Samhitha Babu Mamoon Rashid Ramar Saranya Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization Problem Axioms artificial bee colony Nelder—Mead multi-objective optimization 0-1 knapsack problem nurse scheduling problem |
title | Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization Problem |
title_full | Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization Problem |
title_fullStr | Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization Problem |
title_full_unstemmed | Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization Problem |
title_short | Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization Problem |
title_sort | multi objective abc nm algorithm for multi dimensional combinatorial optimization problem |
topic | artificial bee colony Nelder—Mead multi-objective optimization 0-1 knapsack problem nurse scheduling problem |
url | https://www.mdpi.com/2075-1680/12/4/395 |
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