Improved Salp Swarm Algorithm with Simulated Annealing for Solving Engineering Optimization Problems

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm (SSA), as a swarmbased algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

[1]  Dongxu Zhu,et al.  Multi-Objective Optimization of a Mine Water Reuse System Based on Improved Particle Swarm Optimization , 2021, Sensors.

[2]  Lin Wang,et al.  Sustainable design and optimization of coal supply chain network under different carbon emission policies , 2020 .

[3]  Ye Wang,et al.  Modified Multi-Crossover Operator NSGA-III for Solving Low Carbon Flexible Job Shop Scheduling Problem , 2020, Processes.

[4]  Amir Hossein Gandomi,et al.  Benchmark Problems in Structural Optimization , 2011, Computational Optimization, Methods and Algorithms.

[5]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[6]  Mohamed H. Haggag,et al.  A novel chaotic salp swarm algorithm for global optimization and feature selection , 2018, Applied Intelligence.

[7]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[8]  Guohua Wu,et al.  A test-suite of non-convex constrained optimization problems from the real-world and some baseline results , 2020, Swarm Evol. Comput..

[9]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[10]  Xuehua Zhao,et al.  Chaos-Induced and Mutation-Driven Schemes Boosting Salp Chains-Inspired Optimizers , 2019, IEEE Access.

[11]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[12]  Tao Chen,et al.  Back propagation neural network with adaptive differential evolution algorithm for time series forecasting , 2015, Expert Syst. Appl..

[13]  Botao Ma,et al.  A Comprehensive Improved Salp Swarm Algorithm on Redundant Container Deployment Problem , 2019, IEEE Access.

[14]  J. Zhang,et al.  Improved Salp Swarm Algorithm Based on Levy Flight and Sine Cosine Operator , 2020, IEEE Access.

[15]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[16]  Jaroslaw Sobieszczanski-Sobieski,et al.  Multidisciplinary Design Optimization: An Emerging New Engineering Discipline , 1995 .

[17]  Seyed Mohammad Mirjalili.  How effective is the Grey Wolf optimizer in training multi-layer perceptrons , 2014, Applied Intelligence.

[18]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[19]  Emanuele Garone,et al.  A Distributed Method for Linear Programming Problems With Box Constraints and Time-Varying Inequalities , 2019, IEEE Control Systems Letters.

[20]  Orhan Dengiz,et al.  A tabu search algorithm for the training of neural networks , 2009, J. Oper. Res. Soc..

[21]  Dan Simon,et al.  Structural and Parametric Optimization of Fuzzy Control and Decision Making Systems , 2016, WCSC.

[22]  Arun Kumar Sangaiah,et al.  Krill herd algorithm based on cuckoo search for solving engineering optimization problems , 2017, Multimedia Tools and Applications.

[23]  Ajay K. Ray,et al.  APPLICATIONS OF MULTIOBJECTIVE OPTIMIZATION IN CHEMICAL ENGINEERING , 2000 .

[24]  Hossam Faris,et al.  An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems , 2018, Knowl. Based Syst..

[25]  Songfeng Lu,et al.  Improved salp swarm algorithm based on particle swarm optimization for feature selection , 2018, Journal of Ambient Intelligence and Humanized Computing.

[26]  Hongwei Kang,et al.  A Modified jSO Algorithm for Solving Constrained Engineering Problems , 2020, Symmetry.

[27]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[28]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[29]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[30]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[31]  Guido Moerkotte,et al.  Heuristic and randomized optimization for the join ordering problem , 1997, The VLDB Journal.

[32]  Xingping Sun,et al.  Success History-Based Adaptive Differential Evolution Using Turning-Based Mutation , 2020, Mathematics.

[33]  Liu Hui.  A new evolutionary algorithm for solving constrained optimization problems , 2006 .

[34]  Chang Wook Ahn,et al.  On the practical genetic algorithms , 2005, GECCO '05.

[35]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[36]  Ilya V. Kolmanovsky,et al.  Embedding Constrained Model Predictive Control in a Continuous-Time Dynamic Feedback , 2017, IEEE Transactions on Automatic Control.

[37]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[38]  Constantin F. Aliferis,et al.  The max-min hill-climbing Bayesian network structure learning algorithm , 2006, Machine Learning.

[39]  Santosh Kumar Majhi,et al.  Improved Salp Swarm Algorithm with Space Transformation Search for Training Neural Network , 2019, Arabian Journal for Science and Engineering.

[40]  Neculai Andrei,et al.  Nonlinear Optimization Applications Using the GAMS Technology , 2013 .

[41]  Amir Hossein Gandomi,et al.  Chaotic Krill Herd algorithm , 2014, Inf. Sci..