MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems

Premkumar, M.; Jangir, Pradeep; Sowmya, R.

doi:10.1016/j.knosys.2021.106856

Cited by 75 publications

(36 citation statements)

References 56 publications

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“…In this study, every algorithm is executed 30 times individually for all considered eight test examples with a 40population size, 500 maximum iteration number, and 20,000 functional evaluations [46].  The Hypervolume (HV) and Inverted Generational Difference (IGD) metric are employed to concurrently examine the uniformity-convergence-spread of the non-dominated set of solutions procured from the computation experiments.…”

Section: A Evaluation Methodsmentioning

confidence: 99%

“… The Hypervolume (HV) and Inverted Generational Difference (IGD) metric are employed to concurrently examine the uniformity-convergence-spread of the non-dominated set of solutions procured from the computation experiments.  To examine the search efficiency and reliability of considered algorithms in terms of faster convergence rate Generational Distance (GD), Spread (SD), Coverage (CVG), and Coverage over Pareto Front (CPF) metrics are used [46][47][48].  To measure the computational complexity, Runtime (RT) metric and for combined diversity-spread, spacing (SP), Diversity (DM), and Pure Diversity (PD) metrics are calculated [46][47][48].…”

Section: A Evaluation Methodsmentioning

confidence: 99%

“… To examine the search efficiency and reliability of considered algorithms in terms of faster convergence rate Generational Distance (GD), Spread (SD), Coverage (CVG), and Coverage over Pareto Front (CPF) metrics are used [46][47][48].  To measure the computational complexity, Runtime (RT) metric and for combined diversity-spread, spacing (SP), Diversity (DM), and Pure Diversity (PD) metrics are calculated [46][47][48].  The mean and standard deviation (STD) values of the metrics are regarded as the statistical performance measure [4,15].…”

Section: A Evaluation Methodsmentioning

confidence: 99%

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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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Section: A Evaluation Methodsmentioning

confidence: 99%

Section: A Evaluation Methodsmentioning

confidence: 99%

Section: A Evaluation Methodsmentioning

confidence: 99%

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

Kumar

Jangir²,

Tejani³

et al. 2021

IEEE Access

Self Cite

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“…Algorithm of the Innovative Gunner (AIG) [9], Artificial Electric Field Algorithm (AEFA) [10], and Quantum Henry Gas Solubility (QHGS) [11] are examples of the recently developed metaheuristics. Metaheuristics as probabilistic solvers have been applied and developed for solving optimization problems in the field of civil engineering, such as structural optimization [12,13], damage detection [14,15], optimal design of retaining wall [16,17], etc. Motivated by the potential application of metaheuristics, this study attempts to apply some population-based metaheuristics for the optimal design of the curved roof and pitched roof portal frames.…”

Section: Introductionmentioning

confidence: 99%

Discrete Optimum Design of Planar Steel Curved Roof and Pitched Roof Portal Frames Using Metaheuristic Algorithms

Kaveh

Dastjerdi

Zaerreza

et al. 2021

Period. Polytech. Civil Eng.

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Portal frames are single-story frame buildings including columns and rafters, and their rafters can be either curved or pitched. These are used widely in the construction of industrial buildings, warehouses, gyms, fire stations, agricultural buildings, hangars, etc. The construction cost of these frames considerably depends on their weight. In the present research, the discrete optimum design of two types of portal frames including planar steel Curved Roof Frame (CRF) and Pitched Roof Frame (PRF) with tapered I-section members are presented. The optimal design aims to minimize the weight of these frame structures while satisfying some design constraints based on the requirements of ANSI/AISC 360-16 and ASCE 7-10. Four population-based metaheuristic optimization algorithms are applied to the optimal design of these frames. These algorithms consist of Teaching-Learning-Based Optimization (TLBO), Enhanced Colliding Bodies Optimization (ECBO), Shuffled Shepherd Optimization Algorithm (SSOA), and Water Strider Algorithm (WSA). Two main objectives are followed in this paper. The first one deals with comparing the optimized weight of the CRF and PRF structures with the same dimensions for height and span in two different span lengths (16.0 m and 32.0 m), and the second one is related to comparing the performance of the considered metaheuristics in the optimum design of these portal frames. The obtained results reveal that CRF is more economical than PRF in the fair comparison. Moreover, comparing the results acquired by SSOA with those of other considered metaheuristics reveals that SSOA has better performance for the optimal design of these portal frames.

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“…where q denotes a total number of objectives, m and p denote the number of inequality and equality constraints, respectively, LBi is the lower bound of the i th variable, and UBi is the upper bound of the i th variable. Recently, optimization algorithms have been successfully applied to solve MOPs [8]- [9]. Thanks to their ability to determine a Pareto optimal solution in a specific run, these optimization algorithms tend to be more beneficial than the traditional algorithms.…”

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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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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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MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems

Cited by 75 publications

References 56 publications

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

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

Discrete Optimum Design of Planar Steel Curved Roof and Pitched Roof Portal Frames Using Metaheuristic Algorithms

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

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