ε Constrained differential evolution using halfspace partition for optimization problems

There are many efficient and effective constraint-handling mechanisms for constrained optimization problems. However, most of them evaluate all the individuals, including the worse individuals, which waste a lot of fitness evaluations. In this paper, halfspace partition mechanism based on constraint violation values is proposed. Since constraint violation information of individuals in current generation are already known, the positive side of tangent line of one point as positive halfspace is defined. A point is treated as potential point if it locates in the intersect region of two positive halfspaces. Hence, the region includes all these points has greater possibility to obtain smaller constraint violation. Only when the offspring locates in this area, the actual objective function value and constraint violation will be calculated. The estimated worse individuals will be omitted without calculating actual constraint violation and fitness function value. Four engineering optimization and a case study with the grinding optimization process are studied. The experimental results verify the effectiveness of the proposed mechanism.

[1]  Wenyin Gong,et al.  A multiobjective differential evolution algorithm for constrained optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[2]  Yong Wang,et al.  An improved (μ + λ)-constrained differential evolution for constrained optimization , 2013, Inf. Sci..

[3]  Andrew Y. C. Nee,et al.  Micro-computer-based optimization of the surface grinding process , 1992 .

[4]  Tetsuyuki Takahama,et al.  Solving Difficult Constrained Optimization Problems by the ε Constrained Differential Evolution with Gradient-Based Mutation , 2009 .

[5]  Xin Yao,et al.  Search biases in constrained evolutionary optimization , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Ruhul A. Sarker,et al.  Constraint Consensus Mutation-Based Differential Evolution for Constrained Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[7]  N. Hansen,et al.  Markov Chain Analysis of Cumulative Step-Size Adaptation on a Linear Constrained Problem , 2015, Evolutionary Computation.

[8]  Yong Wang,et al.  Constrained Evolutionary Optimization by Means of ( + )-Differential Evolution and Improved Adaptive Trade-Off Model , 2011, Evolutionary Computation.

[9]  Ivona Brajevic,et al.  An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems , 2018, Journal of Intelligent Manufacturing.

[10]  C. A. Coello Coello,et al.  Multiple trial vectors in differential evolution for engineering design , 2007 .

[11]  Liang Gao,et al.  An improved electromagnetism-like mechanism algorithm for constrained optimization , 2013, Expert Syst. Appl..

[12]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[13]  Ali Rıza Yıldız,et al.  A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects , 2017 .

[14]  Ling Wang,et al.  An effective differential evolution with level comparison for constrained engineering design , 2010 .

[15]  Ali Wagdy Mohamed,et al.  A novel differential evolution algorithm for solving constrained engineering optimization problems , 2017, Journal of Intelligent Manufacturing.

[16]  Dexuan Zou,et al.  A novel modified differential evolution algorithm for constrained optimization problems , 2011, Comput. Math. Appl..

[17]  Ali Wagdy Mohamed,et al.  Constrained optimization based on modified differential evolution algorithm , 2012, Inf. Sci..

[18]  Tetsuyuki Takahama,et al.  Constrained Optimization by the epsilon Constrained Hybrid Algorithm of Particle Swarm Optimization and Genetic Algorithm , 2005, Australian Conference on Artificial Intelligence.

[19]  Liang Gao,et al.  An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes , 2015, Appl. Soft Comput..

[20]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

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

[22]  Qing Zhang,et al.  Constrained optimization by the evolutionary algorithm with lower dimensional crossover and gradient-based mutation , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[23]  Gary G. Yen,et al.  An Adaptive Penalty Formulation for Constrained Evolutionary Optimization , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[24]  Carlos A. Coello Coello,et al.  Constraint-handling in nature-inspired numerical optimization: Past, present and future , 2011, Swarm Evol. Comput..

[25]  Ali Rıza Yıldız,et al.  Comparison of grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod , 2018 .

[26]  Tapabrata Ray,et al.  Infeasibility Driven Evolutionary Algorithm for Constrained Optimization , 2009 .

[27]  Xavier Blasco Ferragud,et al.  Multiobjective optimization algorithm for solving constrained single objective problems , 2010, IEEE Congress on Evolutionary Computation.

[28]  Jing J. Liang,et al.  Problem Deflnitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization , 2006 .

[29]  Tetsuyuki Takahama,et al.  Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[30]  Xinyu Li,et al.  Ε Constrained Differential Evolution with Pre-estimated Comparison Using Gradient-based Approximation for Constrained Optimization Problems , 2016, Expert Syst. Appl..

[31]  Ivan Zelinka,et al.  MIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 1: the optimization method , 2004 .

[32]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[33]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[34]  T. Takahama,et al.  Constrained Optimization by α Constrained Genetic Algorithm (αGA) , 2003 .

[35]  Jing Liu,et al.  An Organizational Evolutionary Algorithm for Numerical Optimization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[36]  R. Haftka,et al.  Constrained particle swarm optimization using a bi-objective formulation , 2009 .

[37]  Gary G. Yen,et al.  Constrained Optimization Via Artificial Immune System , 2014, IEEE Transactions on Cybernetics.

[38]  Quan-Ke Pan,et al.  Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs , 2020, IEEE Transactions on Cybernetics.

[39]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[40]  Jonathan A. Wright,et al.  Self-adaptive fitness formulation for constrained optimization , 2003, IEEE Trans. Evol. Comput..

[41]  Efrén Mezura-Montes,et al.  Differential evolution in constrained numerical optimization: An empirical study , 2010, Inf. Sci..

[42]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[43]  Liang Gao,et al.  Engineering design optimization using an improved local search based epsilon differential evolution algorithm , 2018, J. Intell. Manuf..

[44]  Liang Gao,et al.  Multi-objective optimization based reverse strategy with differential evolution algorithm for constrained optimization problems , 2015, Expert Syst. Appl..

[45]  Yong Wang,et al.  A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization , 2006, IEEE Transactions on Evolutionary Computation.

[46]  Morteza Kiani,et al.  A Comparative Study of Non-traditional Methods for Vehicle Crashworthiness and NVH Optimization , 2016 .

[47]  Wenjian Luo,et al.  Differential evolution with dynamic stochastic selection for constrained optimization , 2008, Inf. Sci..

[48]  Nantiwat Pholdee,et al.  Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame , 2017, International Journal of Vehicle Design.

[49]  Yong Wang,et al.  Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization , 2010, Appl. Soft Comput..

[50]  Ali R. Yildiz,et al.  Comparison of evolutionary-based optimization algorithms for structural design optimization , 2013, Eng. Appl. Artif. Intell..

[51]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[52]  Hui Li,et al.  A lower-dimensional-search evolutionary algorithm and its application in constrained optimization problems , 2007, 2007 IEEE Congress on Evolutionary Computation.

[53]  Angel Eduardo Muñoz Zavala,et al.  Continuous Constrained Optimization with Dynamic Tolerance Using the COPSO Algorithm , 2009 .

[54]  Yong Wang,et al.  Utilizing the Correlation Between Constraints and Objective Function for Constrained Evolutionary Optimization , 2020, IEEE Transactions on Evolutionary Computation.

[55]  Tetsuyuki Takahama,et al.  Constrained Optimization by epsilon Constrained Particle Swarm Optimizer with epsilon-level Control , 2005, WSTST.

[56]  A. Kai Qin,et al.  Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.