A Hybrid Evolutionary Multi-objective and SQP Based Procedure for Constrained Optimization

2007 ◽  
pp. 36-45 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Swanand Lele ◽  
Rituparna Datta
Keyword(s):  
Constrained Optimization ◽  
Multi Objective

scholarly journals Multi-objective Ranking via Constrained Optimization

2020 ◽  
Author(s):  
Michinari Momma ◽  
Alireza Bagheri Garakani ◽  
Nanxun Ma ◽  
Yi Sun
Keyword(s):  
Constrained Optimization ◽  
Multi Objective

scholarly journals Multi-objective constrained optimization for energy applications via tree ensembles

2022 ◽  
Vol 306 ◽  
pp. 118061
Author(s):  
Alexander Thebelt ◽  
Calvin Tsay ◽  
Robert M. Lee ◽  
Nathan Sudermann-Merx ◽  
David Walz ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Multi Objective ◽  
Energy Applications

scholarly journals A framework to use crop models for multi-objective constrained optimization of irrigation strategies

2016 ◽  
Vol 86 ◽  
pp. 145-157 ◽  
Author(s):  
Bruno Cheviron ◽  
R. Willem Vervoort ◽  
Rami Albasha ◽  
Romain Dairon ◽  
Camille Le Priol ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Crop Models ◽  
Multi Objective

New Dynamic Multi-Objective Constrained Optimization Evolutionary Algorithm

2015 ◽  
Vol 32 (05) ◽  
pp. 1550036 ◽  
Author(s):  
Chun-An Liu ◽  
Yuping Wang ◽  
Aihong Ren
Keyword(s):  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Constrained Optimization Problem ◽  
Multi Objective ◽  
Time Period ◽  
Pareto Fronts

For dynamic multi-objective constrained optimization problem (DMCOP), it is important to find a sufficient number of uniformly distributed and representative dynamic Pareto optimal solutions. In this paper, the time period of the DMCOP is first divided into several random subperiods. In each random subperiod, the DMCOP is approximately regarded as a static optimization problem by taking the time subperiod fixed. Then, in order to decrease the amount of computation and improve the effectiveness of the algorithm, the dynamic multi-objective constrained optimization problem is further transformed into a dynamic bi-objective constrained optimization problem based on the dynamic mean rank variance and dynamic mean density variance of the evolution population. The evolution operators and a self-check operator which can automatically checkout the change of time parameter are introduced to solve the optimization problem efficiently. And finally, a dynamic multi-objective constrained optimization evolutionary algorithm is proposed. Also, the convergence analysis for the proposed algorithm is given. The computer simulations are made on four dynamic multi-objective optimization test functions and the results demonstrate that the proposed algorithm can effectively track and find the varying Pareto optimal solutions or the varying Pareto fronts with the change of time.

An Improved Constrained Optimization Multi-Objective Genetic Algorithm and its Application in Engineering

2014 ◽  
Vol 962-965 ◽  
pp. 2903-2908
Author(s):  
Yun Lian Liu ◽  
Wen Li ◽  
Tie Bin Wu ◽  
Yun Cheng ◽  
Tao Yun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Crossover Operator ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Great Ability ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

An improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, our algorithm is based on multi-objective technique, where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local searching ability. Diversity mutation operator is introduced for non-dominated subpopulation. Through testing the performance of the proposed algorithm on 3 benchmark functions and 1 engineering optimization problems, and comparing with other meta-heuristics, the result of simulation shows that the proposed algorithm has great ability of global search. Keywords: multi-objective optimization;genetic algorithm;constrained optimization problem;engineering application

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