A Multi-objective Time-Linkage Approach for Dynamic Optimization Problems with Previous-Solution Displacement Restriction

2018 ◽  
pp. 864-878 ◽  
Author(s):  
Danial Yazdani ◽  
Trung Thanh Nguyen ◽  
Juergen Branke ◽  
Jin Wang
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Dynamic Optimization Problems ◽  
Multi Objective ◽  
Objective Time ◽  
Previous Solution

scholarly journals APPLICATION OF POPULATION EVOLVABILITY IN A HYPER-HEURISTIC FOR DYNAMIC MULTI-OBJECTIVE OPTIMIZATION

2019 ◽  
Vol 25 (5) ◽  
pp. 951-978 ◽  
Author(s):  
Teodoro Macias-Escobar ◽  
Laura Cruz-Reyes ◽  
Bernabé Dorronsoro ◽  
Héctor Fraire-Huacuja ◽  
Nelson Rangel-Valdez ◽  
...  
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Dynamic Properties ◽  
Selection Method ◽  
Multiobjective Evolutionary Algorithms ◽  
Multi Objective Optimization ◽  
Selection Methods ◽  
Dynamic Optimization Problems ◽  
Multi Objective

It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic that uses low-level heuristic (LLH) selection methods that apply population evolvability. DPEM_HH uses dynamic multiobjective evolutionary algorithms (DMOEAs) as LLHs to solve dynamic multi-objective optimization problems (DMOPs). Population evolvability is incorporated in DPEM_HH by calculating it on each candidate DMOEA for a set of sampled generations after a change is detected, using those values to select which LLH will be applied. DPEM_HH is tested on multiple DMOPs with dynamic properties that provide several challenges. Experimental results show that DPEM_HH with a greedy LLH selection method that uses average population evolvability values can produce solutions closer to the Pareto optimal front with equal to or better diversity than previously proposed heuristics. This shows the effectiveness and feasibility of the application of population evolvability on hyperheuristics to solve dynamic optimization problems.

An efficient multi-objective artificial raindrop algorithm and its application to dynamic optimization problems in chemical processes

2017 ◽  
Vol 58 ◽  
pp. 354-377 ◽  
Author(s):  
Qiaoyong Jiang ◽  
Lei Wang ◽  
Yanyan Lin ◽  
Xinhong Hei ◽  
Guolin Yu ◽  
...  
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Chemical Processes ◽  
Dynamic Optimization Problems ◽  
Multi Objective

scholarly journals Solving multi-objective dynamic optimization problems with fuzzy satisfying method

2003 ◽  
Vol 24 (5) ◽  
pp. 279-296 ◽  
Author(s):  
Cheng-Liang Chen ◽  
Chia-Yuan Chang ◽  
Daim-Yuang Sun
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Dynamic Optimization Problems ◽  
Multi Objective

An algorithm comparison for dynamic optimization problems

2012 ◽  
Vol 12 (10) ◽  
pp. 3176-3192 ◽  
Author(s):  
Ignacio G. del Amo ◽  
David A. Pelta ◽  
Juan R. González ◽  
Antonio D. Masegosa
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Dynamic Optimization Problems ◽  
Algorithm Comparison
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