Local Search Direction for Multi-Objective Optimization Using Memetic EMO Algorithms

2005 ◽  
pp. 385-410 ◽  
Author(s):  
Tadahiko Murata ◽  
Shiori Kaige ◽  
Hisao Ishibuchi
Keyword(s):  
Local Search ◽  
Search Direction ◽  
Multi Objective Optimization ◽  
Multi Objective

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.

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

scholarly journals An Improved SPEA2 Algorithm with Local Search for Multi-Objective Investment Decision-Making

2019 ◽  
Vol 9 (8) ◽  
pp. 1675 ◽  
Author(s):  
Xi Liu ◽  
Dan Zhang
Keyword(s):  
Decision Making ◽  
Local Search ◽  
Investment Decision ◽  
Global Search ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Improved Method ◽  
Optimal Boundary ◽  
Multi Objective ◽  
Investment Decision Making

Enterprise investment decision-making should not only consider investment profits, but also investment risks, which is a complex nonlinear multi-objective optimization problem. However, traditional investment decisions often only consider profit as a goal, resulting in an incorrect decision. Facing the high complexity of investment decision-making space, traditional multi-objective optimization methods pay too much attention to global search ability because of pursuing convergence speed and avoiding falling into local optimum, while local search ability is insufficient, which makes it difficult to converge to the Pareto optimal boundary. To solve this problem, an improved SPEA2 algorithm is proposed to optimize the multi-objective decision-making of investment. In the improved method, an external archive set is set up separately for local search after genetic operation, which guarantees the global search ability and also has strong local search ability. At the same time, the new crossover operator and individual update strategy are used to further improve the convergence ability of the algorithm while maintaining a strong diversity of the population. The experimental results show that the improved method can converge to the Pareto optimal boundary and improve the convergence speed, which can effectively realize the multi-objective decision-making of investment.

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