MOSMA: Multi-Objective Slime Mould Algorithm Based on Elitist Non-Dominated Sorting

Premkumar, M.; Jangir, Pradeep; Sowmya, R.; Alhelou, Hassan Haes; Heidari, Ali Asghar; Chen, Huiling

doi:10.1109/access.2020.3047936

Cited by 159 publications

(69 citation statements)

References 81 publications

(76 reference statements)

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“…In order to assess whether the suggested MOAOA is efficient in solving multi-objective optimization problems, several experiments are conducted on the unconstrained ZDT1-4, ZDT6 multi-objective problems with two objectives [52], and the CEC-2021 test problems with two, three, and five objectives [53] with different performance metrics. The proposed MOAOA results are compared with four state-ofthe-art optimizers, namely NSGWO [33], MOMVO [30], MOALO [12], and MOSMA [34]. In the following subsection, the test problems and performance metrics adopted are briefly introduced.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…In general, multiple methods, such as fitness-sharing, clustering [29], and crowding-distance [9], are used to calculate the Spread of optimal solution in PF. Most of the Pareto-based MOMOs are: MOPSO [14], Multi-Objective Multi-Verse Optimizer (MOMVO) [30], Multi-Objective Heat Transfer Search (MOHTS) [31], Nondominated Sorting MFO (NSMFO) [32], Non-dominated Sorting GWO (NSGWO) [33], Multi-Objective Slime Mould Optimizer (MOSMA) [34], MOALO [12], Nondominated Sorting WOA (NSWOA) [35], and Multi-Objective Passing Vehicle Search (MOPVS) [31].…”

Section: Def 3 Pareto Optimality [25]mentioning

confidence: 99%

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A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

Jangir²,

et al. 2021

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In this paper, a new Multi-Objective Arithmetic Optimization Algorithm (MOAOA) is proposed for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such problems can be found in different areas, including mechanical engineering, chemical engineering, process and synthesis, and power electronics systems. MOAOA is inspired by the distribution behavior of the main arithmetic operators in mathematics. The proposed multi-objective version is formulated and developed from the recently introduced single-objective Arithmetic Optimization Algorithm (AOA) through an elitist nondominance sorting and crowding distance-based mechanism. For the performance evaluation of MOAOA, a set of 35 constrained RWMOPs and five ZDT unconstrained problems are considered. For the fitness and efficiency evaluation of the proposed MOAOA, the results obtained from the MOAOA are compared with four other state-of-the-art multi-objective algorithms. In addition, five performance indicators, such as Hyper-Volume (HV), Spread (SP), Inverse Generalized Distance (IGD), Runtime (RT), and Generalized Distance (GD), are calculated for the rigorous evaluation of the performance and feasibility study of the MOAOA. The findings demonstrate the superiority of the MOAOA over other algorithms with high accuracy and coverage across all objectives. This paper also considers the Wilcoxon signed-rank test (WSRT) for the statistical investigation of the experimental study. The coverage, diversity, computational cost, and convergence behavior achieved by MOAOA show its high efficiency in solving ZDT and RWMOPs problems. INDEX TERMS Arithmetic Optimization Algorithm (AOA); CEC-2021 real-world problems; Constrained optimization; Multi-Objective Arithmetic Optimization Algorithm (MOAOA). I. INTRODUCTIONRecently, computer technology advancements have increased the quality of addressing complex problems and decreased the time and cost of producing the optimal solution. However, human input is yet needed to determine the best of different solutions. Significant efforts can be seen in the literature to produce a system that optimally solves the given problem without any human effort [1]. One of the most reliable methods to accomplish this depends on optimization techniques. In many instances, most engineering problems, such as city programming, program management, investment decision, control system design, engineering

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Def 3 Pareto Optimality [25]mentioning

confidence: 99%

A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

Jangir²,

et al. 2021

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“…To examine the convergence, coverage, intensification, and diversification of the proposed MOPGO, numerous 2-D and 3-D structural tests were examined and contrasted with other state-of-the-art MO optimization strategies existing in the literature, viz. MOPVS [4], MOSMA [45], MOSOS [25], and MOALO [23]. The subsequent section elaborates on the eight truss problems, i.e.…”

Section: Empirical Evaluationmentioning

confidence: 99%

MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems

Kumar

Jangir²,

Tejani³

et al. 2021

IEEE Access

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A new multi-objective Plasma Generation Optimization (MOPGO) algorithm is suggested, and its non-dominated sorting (NDS) mechanism is investigated for numerous challenging real-world structural optimization design issues. The recently developed physics-based Plasma Generation Optimization (PGO) algorithm that works on excitation modes, deexcitation, and ionization plasma production systems is the inspiration behind this endeavor. As the search progresses, a better balance between exploration and exploitation has a more significant impact on the results; thus, the crowding distance feature is incorporated in the proposed MOPGO. Also, the proposed posteriori method exercises a non-dominated sorting strategy to preserve population diversity, which is a crucial problem in multi-objective meta-heuristics. In truss design problems, minimization of the truss's mass and maximization of nodal displacement are considered as objective functions. In contrast, elemental stress and discrete cross-sectional areas are assumed to be behavior and side constraints, respectively. The usefulness of the MOPGO to solve complex problems is validated by eight truss-bar design problems. The efficacy of the MOPGO is evaluated based on ten performance metrics. The results demonstrate that the proposed MOPGO algorithm achieves the optimal solution with less computational complexity and has a better convergence, coverage, diversity, and spread. The Pareto fronts of MOPGO are compared and contrasted with multi-objective passing vehicle search, multi-objective slime mould algorithm, multi-objective symbiotic organisms search, and multi-objective ant lion optimization algorithms.

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“…The algorithm [26] has been commonly used since it was proposed [77], [81], [113], such as feature selection [78], energy management [114]. In addition, several improved versions of SMA were introduced, for example, hybrid SMA-based whale optimization algorithm [81], adaptive guided differential evolution algorithm with SMA [77], chaos-opposition-enhanced SMA [115], multiobjective SMA Based on elitist non-dominated sorting [116], and improved SMA with Levy flight [117].…”

Section: Proposed Esmamentioning

confidence: 99%

Early Recognition and Discrimination of COVID-19 Severity Using Slime Mould Support Vector Machine for Medical Decision-Making

et al. 2021

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has spread rapidly across the world, leading to the insufficiency of medical resources in many regions. Early detection and identification of high-risk COVID-19 patients will contribute to early intervention and optimize medical resource allocation. Using the clinical data from the Affiliated Yueqing Hospital of Wenzhou Medical University (Yueqing, China), an evolutionary support vector machine model is designed to recognize and discriminate the severity of the COVID-19 by patients basic information and hematological indexes. The support vector machine is a frequently used pattern classification tool affected by both the kernel parameter setting and feature selection for its classification accuracy. This study recommends an enhanced Slime Mould Algorithm (ESMA), mixing a new movement strategy of white holes, black holes, and wormholes, to perform parameter optimization and feature selection simultaneously for SVM. Therefore, the proposed SVM framework (ESMA-SVM) can also obtain highquality classification results, and it is less prone to stagnation in the classification process. To verify the capabilities of the proposed methodology, first, the performance of the ESMA is thoroughly verified by using IEEE CEC2017 benchmark functions and the diversity and compared with other similar methods experimentally using these standard benchmark functions. Moreover, the balance between diversification and intensification capability of the enhanced ESMA and the original SMA is also investigated statistically. Finally, the designed model ESMA-SVM and other competitive SVM models based on other optimization algorithms are applied to early recognition and discrimination of COVID-19 severity. Through the analysis of experimental results, the core compensations of ESMA are confirmed, and the ESMA-SVM can obtain strong performance in terms of several performance evaluation indexes on discrimination of COVID-19 severity.

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MOSMA: Multi-Objective Slime Mould Algorithm Based on Elitist Non-Dominated Sorting

Cited by 159 publications

References 81 publications

A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems

Early Recognition and Discrimination of COVID-19 Severity Using Slime Mould Support Vector Machine for Medical Decision-Making

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