DTCSMO: An efficient hybrid starling murmuration optimizer for engineering applications

Hu, Gang; Zhong, Jingyu; Wei, Guo; Chang, Ching-Ter

doi:10.1016/j.cma.2023.115878

Cited by 36 publications

(11 citation statements)

References 95 publications

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“…For performance evaluation, four engineering design problems including, (1) pressure vessel design, (2) rolling element bearing design, (3) tension/compression spring design, and (4) cantilever beam design, are used. The MHDE algorithm is compared with respect to some of the well-known algorithms including, artificial rabbit optimization (ARO) 55 , taguchi search algorithm (TSA) 56 , multi-strategy chameleon algorithm (MCSA) 56 , hybrid particle swarm optimization (HPSO) 57 , equilibrium optimizer (EO) 21 , evolution strategies (ES) 58 , grasshopper optimization algorithm (GOA) 59 , ( ) evolutionary search (ES) 60 , harris hawk optimizer (HHO) 56 , cuckoo search (CS) 55 , GCAII 55 , ant colony optimization (ACO) 55 , co-evolutionary DE (CDE) 60 , bacterial foraging optimization algorithm (BFOA) 61 , symbiotic optimization search (SOS) 62 , passing vehicle search (PVS) 63 , meerkat optimization algorithm (MOA) 64 , red panda optimizer (RPO) 65 , mine blast algorithm (MBA) 66 , moth flame optimizer (MFO) 56 , thermal exchange optimization (TEO) 67 , GCAI 55 , co-evolutionary differential evolution (CDE) 60 , seagull optimization algorithm (SOA) 68 , co-evolutionary particle swarm optimization approach (CPSO) 57 , and dynamic opposition strategy taylor-based optimal neighbourhood strategy and crossover operator (DTCSMO) 69 .…”

Section: Real-world Applications I: Engineering Design Problemsmentioning

confidence: 99%

A novel multi-hybrid differential evolution algorithm for optimization of frame structures

Salgotra,

Gandomi

2024

Sci Rep

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Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others.

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Section: Real-world Applications I: Engineering Design Problemsmentioning

confidence: 99%

A novel multi-hybrid differential evolution algorithm for optimization of frame structures

Salgotra,

Gandomi

2024

Sci Rep

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“…First, the CEC2014 test function [ 33 ] was tested. Second, to make the experimental results more persuasive, a test on the CEC2017 test function [ 34 ] is introduced to lessen the experiment’s randomness and contingency.…”

Section: Numerical Experiments and Analysismentioning

confidence: 99%

“…Three efficient strategies, i.e., good point set, Cauchy mutation, and differential evolution are applied to the UCDCPA to tackle the complex optimization tasks effectively. The performance of the UCDCPA is checked against the CEC2014 [ 33 ] and CEC2017 [ 34 ] test functions. The experimental results are compared with state-of-the-art algorithms, and some statistical analysis is carried out.…”

Section: Introductionmentioning

confidence: 99%

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Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs

et al. 2023

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Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational costs reasonable. The carnivorous plant algorithm (CPA) is a newly proposed metaheuristic algorithm, inspired by its foraging strategies of attraction, capture, digestion, and reproduction. However, the CPA is not without its shortcomings. In this paper, an enhanced multistrategy carnivorous plant algorithm called the UCDCPA is developed. In the proposed framework, a good point set, Cauchy mutation, and differential evolution are introduced to increase the algorithm’s calculation precision and convergence speed as well as heighten the diversity of the population and avoid becoming trapped in local optima. The superiority and practicability of the UCDCPA are illustrated by comparing its experimental results with several algorithms against the CEC2014 and CEC2017 benchmark functions, and five engineering designs. Additionally, the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank-sum tests. The findings show that these introduced strategies provide some improvements in the performance of the CPA, and the accuracy and stability of the optimization results provided by the proposed UCDCPA are competitive against all algorithms. To conclude, the proposed UCDCPA offers a good alternative to solving optimization issues.

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“…Optimization is finding a suitable set of variable values to minimize (maximize) the value of some optimization objective under certain constraints. Optimization algorithms are widely used in engineering design 1 – 3 , engineering practice 4 – 6 , motion control 7 – 9 , and task scheduling in the real world 10 – 12 . At this stage, algorithms for solving optimization problems contain two main categories 13 .…”

Section: Introductionmentioning

confidence: 99%

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Su,

Dong,

Liu

et al. 2024

Sci Rep

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The fitness function value is a kind of important information in the search process, which can be more targeted according to the guidance of the fitness function value. Most existing meta-heuristic algorithms only use the fitness function value as an indicator to compare the current variables as good or bad but do not use the fitness function value in the search process. To address this problem, the mathematical idea of the fitting is introduced into the meta-heuristic algorithm, and a symmetric projection optimizer (SPO) is proposed to solve numerical optimization and engineering problems more efficiently. The SPO algorithm mainly utilizes a new search mechanism, the symmetric projection search (SP) method. The SP method quickly completes the fitting of the projection plane, which is located through the symmetry of the two points and finds the minima in the projection plane according to the fitting result. Fitting by using the fitness function values allows the SP to find regions where extreme values may exist more quickly. Based on the SP method, exploration and exploitation strategies are constructed, respectively. The exploration strategy is used to find better regions, and the exploitation strategy is used to optimize the discovered regions continuously. The timing of the use of the two strategies is designed so that the SPO algorithm can converge faster while avoiding falling into local optima. The effectiveness of the SPO algorithm is extensively evaluated using seven test suites, including CEC2017, CEC2019, CEC2020, and CEC2022. It is also compared with two sets of 19 recent competitive algorithms. Statistical analyses are performed using five metrics such as the Wilcoxon test, the Friedman test, and variance. Finally, the practicality of the SPO algorithm is verified by four typical engineering problems and a real spacecraft trajectory optimization problem. The results show that the SPO algorithm can find superior results in 94.6% of the comparison tests and is a promising alternative for solving real-world problems.

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DTCSMO: An efficient hybrid starling murmuration optimizer for engineering applications

Cited by 36 publications

References 95 publications

A novel multi-hybrid differential evolution algorithm for optimization of frame structures

A novel multi-hybrid differential evolution algorithm for optimization of frame structures

Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

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