A novel variant of the salp swarm algorithm for engineering optimization

• Autorzy: Jia Fuyun , Luo Sheng , Yin Guan , Ye Yin

Identyfikatory

DOI

10.2478/jaiscr-2023-0011

• Języki publikacji: EN

Abstrakty

EN

There are many design problems need to be optimized in various fields of engineering, and most of them belong to the NP-hard problem. The meta-heuristic algorithm is one kind of optimization method and provides an effective way to solve the NP-hard problem. Salp swarm algorithm (SSA) is a nature-inspired algorithm that mimics and mathematically models the behavior of slap swarm in nature. However, similar to most of the meta-heuristic algorithms, the traditional SSA has some shortcomings, such as entrapment in local optima. In this paper, the three main strategies are adopted to strengthen the basic SSA, including chaos theory, sine-cosine mechanism and the principle of quantum computation. Therefore, the SSA variant is proposed in this research, namely SCQ-SSA. The representative benchmark functions are employed to test the performances of the algorithms. The SCQ-SSA are compared with the seven algorithms in high-dimensional functions (1000 dimensions), seven SSA variants and six advanced variants on benchmark functions, the experiment reveals that the SCQ-SSA enhances resulting precision and alleviates local optimal problems. Besides, the SCQ-SSA is applied to resolve three classical engineering problems: tubular column design problem, tension/compression spring design problem and pressure vessel design problem. The design results indicate that these engineering problems are optimized with high accuracy and superiority by the improved SSA. The source code is available in the URL: https://github.com/ye-zero/SCQSSA/tree/main/SCQ-SSA.

Słowa kluczowe

EN

salp swarm algorithm; meta-heuristic algorithm; chaos theory; sine-cosine mechanism; quantum computation; optimization design of engineering

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2023
• Tom: Vol. 13. No. 3
• Strony: 131--149
• Opis fizyczny: Bibliogr. 64 poz., rys.

Twórcy

autor

Jia Fuyun

• Department of Public Infrastructure, Henan Medical College Zhengzhou 451191 Henan, China

• https://orcid.org/0009-0004-9413-1462

autor

Luo Sheng

• School of Information Science and Technology, Nantong University Nantong 226019 Jiangsu, China

• https://orcid.org/0009-0002-1498-9684

autor

Yin Guan

• School of Energy Resources, China University of Geosciences (Beijing) Beijing 100083 Beijing, China

• https://orcid.org/0009-0002-7853-7767

autor

Ye Yin

• School of Computer Science and Artificial Intelligence, Wuhan University of Technology Wuhan 430070 Hubei China

• https://orcid.org/0000-0002-7653-3669

Bibliografia

• [1] Luo Q, Rao Y, Peng D. GA and GWO algorithm for the special bin packing problem encountered in field of aircraft arrangement. Applied Soft Computing, 2022, 114: 108060.
• [2] Guo H, Hou X, Cao Z, et al. GP3: Gaussian process path planning for reliable shortest path in transportation networks. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8):11575-11590.
• [3] Shanthi J, Rani D G N, Rajaram S. An Enhanced Memetic Algorithm using SKB tree representation for fixed-outline and temperature driven nonslicing floorplanning. Integration, 2022, 86:84-97.
• [4] Li L, Cai Y, Zhou Q. A survey on machine learning-based routing for VLSI physical design. Integration, 2022, 86:51-56.
• [5] Muhammad, Yasir and Raja, Muhammad Asif Zahoor and Altaf, Muhammad et al. Design of fractional comprehensive learning PSO strategy for optimal power flow problems. Applied Soft Computing, 2022, 130:109638.
• [6] Javed S, Ishaque K. A comprehensive analyses with new findings of different PSO variants for MPPT problem under partial shading. Ain Shams Engineering Journal, 2022, 13(5): 101680.
• [7] Ye Y, Huang Q, Rong Y, et al. Field detection of small pests through stochastic gradient descent with genetic algorithm. Computers and Electronics in Agriculture, 2023, 206: 107694.
• [8] Deng W, Zhang X, Zhou Y, et al. An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems. Information Sciences, 2022, 585: 441-453.
• [9] Jiang Y, Wu Q, Zhu S, et al. Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems. Expert Systems with Applications, 2022, 188: 116026.
• [10] Braik M, Hammouri A, Atwan J, et al. White Shark Optimizer: A novel bio-inspired metaheuristic algorithm for global optimization problems. Knowledge-Based Systems, 2022, 243: 108457.
• [11] Wang L, Cao Q, Zhang Z, et al. Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 2022, 114: 105082.
• [12] Shuan-Jun Song and Cheng-Hong Qiu and LongGuang Peng et al. An Assembly Line Multi-Station Assembly Sequence Planning Method Based on Particle Swarm Optimization Algorithm. Journal of Computers, 2022, 33: 115-125.
• [13] Xu S F, Jiang Y N. An Optimization Method of Knowledge Mapping Relationship Based on Improved Ant Colony Algorithm. Journal of Computers, 2022, 33(2): 137-147.
• [14] Ke G, Chen R S, Chen Y C, et al. Network Security Situation Prediction Method Based on Support Vector Machine Optimized by Artificial Bee Colony Algorithms. Journal of Computers, 2021, 32(1): 144-153.
• [15] Mirjalili S, Gandomi A H, Mirjalili S Z, et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in engineering software, 2017, 114: 163-191.
• [16] Mahajan S, Mittal N, Salgotra R, et al. An efficient adaptive salp swarm algorithm using type II fuzzy entropy for multilevel thresholding image segmentation. Computational and Mathematical Methods in Medicine, 2022, 2022.
• [17] Nayak S, Kar S K, Dash S S, et al. Enhanced Salp Swarm Algorithm for Multimodal Optimization and Fuzzy Based Grid Frequency Controller Design. Energies, 2022, 15(9): 3210.
• [18] Ponnusamy M, Bedi P, Suresh T, et al. Design and analysis of text document clustering using salp swarm algorithm. The Journal of Supercomputing, 2022: 1-17.
• [19] Zhang J, Liu W, Tian Z, et al. Urban Rail Substation Parameter Optimization by Energy Audit and Modified Salp Swarm Algorithm. IEEE Transactions on Power Delivery, 2022.
• [20] Abdelkader E M, Moselhi O, Marzouk M, et al. An exponential chaotic differential evolution algorithm for optimizing bridge maintenance plans. Automation in Construction, 2022, 134: 104107.
• [21] Khalaf K S, Sharif M A, Wahhab M S. Digital Communication Based on Image Security usingGrasshopper Optimization and Chaotic Map. International Journal of Engineering, 2022, 35(10): 1981-1988.
• [22] Alshammari M E, Ramli M A M, Mehedi I M. Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem. Energies, 2022, 15(13): 4578.
• [23] Kohli, Mehak and Arora, Sankalap. Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering, 2018, 5(4): 458-472.
• [24] W. Ding and C. Lin and M. Prasad. A LayeredCoevolution-Based Attribute-Boosted Reduction Using Adaptive Quantum Behavior PSO and Its Consistent Segmentation for Neonates Brain Tissue. IEEE Transactions on Fuzzy Systems, 2018, 26(3): 1177-1191.
• [25] K. Srikanth and L. K. Panwar and B. Panigrahi. Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem. Computers & Electrical Engineering, 2018, 70: 243-260.
• [26] D. Zouache and F. Nouioua and A Moussaoui. Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems. Soft Computing, 2016, 20(7): 2781-2799.
• [27] Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Information sciences, 2009, 179(13): 2232-2248.
• [28] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Advances in engineering software, 2014, 69: 46-61.
• [29] Gandomi A H, Yang X S, Alavi A H. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with computers, 2013, 29: 17-35.
• [30] Mirjalili S, Lewis A. The whale optimization algorithm. Advances in engineering software, 2016, 95: 51-67.
• [31] Mirjalili S. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 2016, 96: 120-133.
• [32] Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 2015, 89: 228-249.
• [33] Abualigah L, Diabat A, Mirjalili S, et al. The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 2021, 376: 113609.
• [34] Nautiyal B, Prakash R, Vimal V, et al. Improved salp swarm algorithm with mutation schemes for solving global optimization and engineering problems. Engineering with Computers, 2021: 1-23.
• [35] Wu J, Nan R, Chen L. Improved salp swarm algorithm based on weight factor and adaptive mutation. Journal of Experimental & Theoretical Artificial Intelligence, 2019, 31(3): 493-515.
• [36] Zhang D, Chen Z, Xin Z, et al. Salp swarm algorithm based on craziness and adaptive. Control and Decision, 2020, 35(9): 2112-2120.
• [37] Wang C, Xu R, Ma L, et al. An efficient salp swarm algorithm based on scale-free informed followers with self-adaption weight. Applied Intelligence, 2023, 53(2): 1759-1791.
• [38] Aydemir S B. A novel arithmetic optimization algorithm based on chaotic maps for global optimization. Evolutionary Intelligence, 2022: 1-16.
• [39] Agushaka J O, Ezugwu A E, Abualigah L. Dwarf mongoose optimization algorithm. Computer methods in applied mechanics and engineering, 2022, 391: 114570.
• [40] Shami T M, Mirjalili S, Al-Eryani Y, et al. Velocity pausing particle swarm optimization: a novel variant for global optimization. Neural Computing and Applications, 2023: 1-31.
• [41] Sarma R, Bhargava C, Jain S, et al. Application of ameliorated Harris Hawks optimizer for designing of low-power signed floating-point MAC architecture. Neural Computing and Applications, 2021, 33: 8893-8922.
• [42] Nandi A, Kamboj V K. A Canis lupus inspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem. International Journal for Numerical Methods in Engineering, 2021, 122(4): 1051-1088.
• [43] Kamboj V K, Nandi A, Bhadoria A, et al. An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Applied Soft Computing, 2020, 89: 106018.
• [44] Gandomi A H, Alavi A H. Krill herd: a new bio-inspired optimization algorithm. Communications in nonlinear science and numerical simulation, 2012, 17(12): 4831-4845.
• [45] Gandomi A H. Interior search algorithm (ISA): a novel approach for global optimization. ISA transactions, 2014, 53(4): 1168-1183.
• [46] Rocha A M A C, Fernandes E M G P. Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems. International Journal of Computer Mathematics, 2009, 86(10-11): 1932-1946.
• [47] Meng O K, Pauline O, Kiong S C, et al. Application of modified flower pollination algorithm on mechanical engineering design problem, IOP conference series: materials science and engineering. IOP Publishing, 2017, 165(1): 012032.
• [48] Xu Y, Liu H, Xie S, et al. Competitive search algorithm: a new method for stochastic optimization. Applied Intelligence, 2022, 52(11): 12131-12154.
• [49] Zhongyang J, Zixing C, Yong W. Hybrid selfadaptive orthogonal genetic algorithm for solving global optimization problems. Journal of Software, 2010, 21(6): 1296-1307.
• [50] Zhang M, Wang D, Yang J. Hybrid-flash butterfly optimization algorithm with logistic mapping for solving the engineering constrained optimization problems. Entropy, 2022, 24(4): 525.
• [51] Zhang Y. Elite archives-driven particle swarm optimization for large scale numerical optimization and its engineering applications. Swarm and Evolutionary Computation, 2023, 76: 101212.
• [52] Minh H L, Sang-To T, Theraulaz G, et al. Termite life cycle optimizer. Expert Systems with Applications, 2023, 213: 119211.
• [53] Seyyedabbasi A, Kiani F. Sand Cat swarm optimization: A nature-inspired algorithm to solveglobal optimization problems. Engineering with
• [54] Li C, Liang K, Chen Y, et al. An exploitationboosted sine cosine algorithm for global optimization. Engineering Applications of Artificial Intelligence, 2023, 117: 105620.
• [55] Yang X, Wang R, Zhao D, et al. An adaptive quadratic interpolation and rounding mechanism sine cosine algorithm with application to constrained engineering optimization problems. Expert Systems with Applications, 2023, 213: 119041.
• [56] Liu X, Wang G G, Wang L. LSFQPSO: quantum particle swarm optimization with optimal guided Levy flight and straight flight for solving optimization problems. Engineering with Computers, 2022, 38(Suppl 5): 4651-4682.
• [57] Zhang X, Zhao K, Niu Y. Improved Harris Hawks optimization based on adaptive cooperative foraging and dispersed foraging strategies. IEEE Access, 2020, 8: 160297-160314.
• [58] Chu S C, Xu X W, Yang S Y, et al. Parallel fish migration optimization with compact technology based on memory principle for wireless sensor networks. Knowledge-Based Systems, 2022, 241: 108124.
• [59] Akgung ¨ or A P, Korkmaz E. Bezier Search Differential Evolution algorithm based estimation models of delay parameter k for signalized intersections. Concurrency and Computation: Practice and Experience, 2022, 34(13): e6931.
• [60] Liu H, Zhang X W, Liang H, et al. Stability analysis of the human behavior-based particle swarm optimization without stagnation assumption. Expert Systems with Applications, 2020, 159: 113638.
• [61] Abualigah L, Shehab M, Diabat A, et al. Selection scheme sensitivity for a hybrid Salp Swarm Algorithm: analysis and applications. Engineering with Computers, 2022, 38(2): 1149-1175.
• [62] Moosavi S H S, Bardsiri V K. Poor and rich optimization algorithm: A new human-based and multi populations algorithm. Engineering Applications of Artificial Intelligence, 2019, 86: 165-181.
• [63] Tu J, Chen H, Liu J, et al. Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance. Knowledge-Based Systems, 2021, 212: 106642.
• [64] Castelli M, Manzoni L, Mariot L, et al. Salp swarm optimization: a critical review. Expert Systemswith Applications, 2022, 189: 116029.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).