scholarly journals Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

2021 ◽  
Author(s):  
Qinghua Gu ◽  
Qian Wang ◽  
Neal N. Xiong ◽  
Song Jiang ◽  
Lu Chen
Keyword(s):  
Evolutionary Algorithm ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Multi Objective ◽  
Random Forest Models ◽  
Probability Operator ◽  
Expensive Optimization ◽  
Optimization Efficiency ◽  
Discrete Optimization Problems

AbstractSurrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

scholarly journals Balancing Exploration and Exploitation With Decomposition-Based Dynamic Multi-Objective Evolutionary Algorithm

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Qing Zhang ◽  
Ruwang Jiao ◽  
Sanyou Zeng ◽  
Zhigao Zeng
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Early Stage ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Exploration And Exploitation ◽  
Multi Objective ◽  
Count Function

Balancing exploration and exploitation is a crucial issue in evolutionary global optimization. This paper proposes a decomposition-based dynamic multi-objective evolutionary algorithm for addressing global optimization problems. In the proposed method, the niche count function is regarded as a helper objective to maintain the population diversity, which converts a global optimization problem to a multi-objective optimization problem (MOP). The niche-count value is controlled by the niche radius. In the early stage of evolution, a large niche radius promotes the population diversity for better exploration; in the later stage of evolution, a niche radius close to 0 focuses on convergence for better exploitation. Through the whole evolution process, the niche radius is dynamically decreased from a large value to zero, which can provide a sound balance between the exploration and exploitation. Experimental results on CEC 2014 benchmark problems reveal that the proposed algorithm is capable of offering high-quality solutions, in comparison with four state-of-the-art algorithms.

scholarly journals Balancing Exploration and Exploitation with Decomposition-Based Dynamic Multi-objective Evolutionary Algorithm

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Early Stage ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Exploration And Exploitation ◽  
Multi Objective ◽  
Count Function

Balancing exploration and exploitation is a crucial issue in evolutionary global optimization. This paper proposes a decomposition-based dynamic multi-objective evolutionary algorithm for addressing global optimization problems. In the proposed method, the niche count function is regarded as a helper objective to maintain the population diversity, which converts a global optimization problem to a multi-objective optimization problem (MOP). The niche-count value is controlled by the niche radius. In the early stage of evolution, a large niche radius promotes the population diversity for better exploration; in the later stage of evolution, a niche radius close to 0 focuses on convergence for better exploitation. Through the whole evolution process, the niche radius is dynamically decreased from a large value to zero, which can provide a sound balance between the exploration and exploitation. Experimental results on CEC 2014 benchmark problems reveal that the proposed algorithm is capable of offering high-quality solutions, in comparison with four state-of-the-art algorithms.

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