scholarly journals Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

2013 ◽  
pp. 97-117 ◽  
Author(s):  
Jérémie Dubois-Lacoste ◽  
Manuel López-Ibáñez ◽  
Thomas Stützle
Keyword(s):  
Local Search ◽  
Multi Objective Optimization ◽  
Two Phase ◽  
Multi Objective ◽  
Pareto Local Search

scholarly journals Queued Pareto Local Search for Multi-Objective Optimization

2014 ◽  
pp. 589-599 ◽  
Author(s):  
Maarten Inja ◽  
Chiel Kooijman ◽  
Maarten de Waard ◽  
Diederik M. Roijers ◽  
Shimon Whiteson
Keyword(s):  
Local Search ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Local Search

Multi-objective particle swarm optimization using Pareto-based set and aggregation approach

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740073 ◽  
Author(s):  
Song Huang ◽  
Yan Wang ◽  
Zhicheng Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Multi Objective

Multi-objective optimization problems (MOPs) need to be solved in real world recently. In this paper, a multi-objective particle swarm optimization based on Pareto set and aggregation approach was proposed to deal with MOPs. Firstly, velocities and positions were updated similar to PSO. Then, global-best set was defined in particle swarm optimizer to preserve Pareto-based set obtained by the population. Specifically, a hybrid updating strategy based on Pareto set and aggregation approach was introduced to update the global-best set and local search was carried on global-best set. Thirdly, personal-best positions were updated in decomposition way, and global-best position was selected from global-best set. Finally, ZDT instances and DTLZ instances were selected to evaluate the performance of MULPSO and the results show validity of the proposed algorithm for MOPs.

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