Solving binary multi-objective knapsack problems with novel greedy strategy

2021 ◽  
Author(s):  
Jiawei Yuan ◽  
Yifan Li
Keyword(s):  
Knapsack Problems ◽  
Greedy Strategy ◽  
Multi Objective

Population Decomposition-Based Greedy Approach Algorithm for the Multi-Objective Knapsack Problems

2017 ◽  
Vol 31 (04) ◽  
pp. 1759006 ◽  
Author(s):  
Jiawei Yuan ◽  
Hai-Lin Liu ◽  
Chaoda Peng
Keyword(s):  
Pareto Front ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Weight Vector ◽  
Knapsack Problems ◽  
Multi Objective Optimization ◽  
Greedy Approach ◽  
Greedy Strategy ◽  
Multi Objective ◽  
Repair Strategy

Despite the effectiveness of the decomposition-based multi-objective evolutional algorithm (MOEA/D-M2M) in solving continuous multi-objective optimization problems (MOPs), its performance in addressing 0/1 multi-objective knapsack problems (MOKPs) has not been fully explored. In this paper, we use MOEA/D-M2M with an improved greedy repair strategy to solve MOKPs. It first decomposes an MOKP into a number of simple optimization subproblems and solves them in a collaborative way. Each subproblem has its own subpopulation, and then an improved greedy strategy is introduced to improve the performance of the proposed algorithm on MOKPs. Therein, a weight vector chosen randomly from a corresponding subpopulation is utilized to repair infeasible individuals or improve feasible individuals to have a better fitness, which improves the convergence of the population. Experimental studies on a set of test instances indicate that the MOEA/D-M2M with the improved greedy strategy is superior to MOGLS and MOEA/D in terms of finding better approximations to the Pareto front.

Quantum Immune Clonal Selection Algorithm for Multi-objective 0/1 Knapsack Problems

2010 ◽  
Vol 27 (1) ◽  
pp. 010308 ◽  
Author(s):  
Shang Rong-Hua ◽  
Jiaoli-Cheng ◽  
Li Yang-Yang ◽  
Wu Jian-She
Keyword(s):  
Clonal Selection ◽  
Knapsack Problems ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Multi Objective
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