Multi-objective modified heat transfer search for truss optimization

Kumar, Sumit; Tejani, Ghanshyam G.; Pholdee, Nantiwat; Bureerat, Sujin

doi:10.1007/s00366-020-01010-1

Cited by 52 publications

(16 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Eq. (5) shows that as iteration increases, the search domain around better electrons shrinks. The excitation method demonstrates the algorithm's ability to intensify by reducing the size of d-orbitals.…”

Section: Excitation Processmentioning

confidence: 99%

“…First, a standard optimization technique should be developed to achieve a high-efficiency solution by finding a middle ground between various design objectives, while the second task is to choose the best choice out of all those given to you. As a result, the best option should come from the Pareto optimal points, and a decision-maker should choose it [5]. MO optimization problems are highly complicated due to many and diverse objectives [6].…”

Section: Introductionmentioning

confidence: 99%

“…Few examples are MO improved Heat Transfer Search (HTS) [24], MO adaptive symbiotic organisms search [25], Hybrid HTS and Passing Vehicle Search (PVS) [1], Grey Wolf Optimizer based on nondominated sorting technique [26], enhanced chaotic JAYA algorithm [27], hybrid PSO-multi verse optimizer [28], and many more. A fine balance between global diversification and local intensification is essential for MHs [4,5]. In principle, the terminology diversification corresponds to search space exploration, while the expression intensification leads to the utilization of the cumulative search knowledge.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

Section: Excitation Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…end while (20).Terminate one objective function, the solution is superior (dominate) to the other solution [40]- [43]. The mathematical definition of Pareto optimality for minimization problem is as follows [44]- [46].…”

Section: A Definition Of Multi-objective Optimization Problemmentioning

confidence: 99%

“…Mean relative deviation index is applied to measure the stability of the algorithm. The mean relative deviation index of each objective is calculated by equation (46).…”

Section: ) Mean Relative Deviation Indexmentioning

confidence: 99%

A Discrete Multi-Objective Rider Optimization Algorithm for Hybrid Flowshop Scheduling Problem Considering Makespan, Noise and Dust Pollution

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Many optimization algorithms have been proposed to solve hybrid flowshop scheduling problem (HFSP). However, with the development of industry and society, labor right and labor safety have become important problem to consider in production scheduling. So the green HFSP considering makespan, noise and dust pollution becomes an urgent problem to be solved. In this paper, the rider optimization algorithm (ROA) is modified into the multi-objective rider optimization algorithm (MOROA) using Pareto archive and neighborhood sorting techniques. The Pareto archive and neighborhood sorting technology make the Pareto optimal solution set of MOROA have higher coverage and more solutions. Then MOROA is discretized into discrete MOROA (DMOROA) to solve the HFSP considering makespan, noise and dust pollution. DMOROA is tested on 10, 30 and 50 jobs HFSP considering makespan, noise and dust pollution. The test results are compared with two multi-objective algorithms to verify the performance of DMOROA. And the test results verify that the DMOROA is superior to the comparison algorithms in search accuracy, number of non-dominated solutions, diversity of solution set and stability. Therefore, DMOROA is effective in solving multi-objective HFSP considering makespan, noise and dust pollution. INDEX TERMS Hybrid flowshop scheduling problem, multi-objective meta-heuristic algorithm, rider optimization algorithm, labor right.

show abstract