Solution of Large-scale Many-objective Optimization Problems Based on Dimension Reduction and Solving Knowledge Guided Evolutionary Algorithm

Author(s):  
Xiangjuan Yao ◽  
Qian Zhao ◽  
Dunwei Gong ◽  
Song Zhu
Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Wali Khan Mashwani ◽  
Zia Ur Rehman ◽  
Maharani A. Bakar ◽  
Ismail Koçak ◽  
Muhammad Fayaz

Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 203369-203381
Author(s):  
Mohamed A. Meselhi ◽  
Saber M. Elsayed ◽  
Ruhul A. Sarker ◽  
Daryl L. Essam

2020 ◽  
Vol 11 (2) ◽  
pp. 56-76
Author(s):  
Benkanoun Yazid ◽  
Bouroubi Sadek ◽  
Chaabane Djamal

The authors propose a computing approach for solving a multiobjective problem in the telecommunication network field, suggested by an Algerian industrial company. The principal goal is in developing a palliative solution to overcome some generated problems existing in the current management system. A mathematical operational model has been established. The exact algorithms that solve multiobjective optimization problems are not appropriate for large scale problems. However, the application of metaheuristics approach leads perfectly to approximate the Pareto optimal set. In this paper, the authors apply a well-known multiobjective evolutionary algorithm, the Non-dominated Sorting Genetic Algorithm (NSGA-II), compare the obtained results with those generated by the Strength Pareto Evolutionary Algorithm-II (SPEA2) and propose a way to help the decision maker, who is often confronted with the choice of a final solution, to make his preferences afterward using a utility function based on a Choquet integral measure. Finally, numerical experiments are presented to validate the approach.


Author(s):  
Paul Cronin ◽  
Harry Woerde ◽  
Rob Vasbinder

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