{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T12:26:42Z","timestamp":1768652802594,"version":"3.49.0"},"reference-count":20,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2017,4]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0218001417590066","type":"journal-article","created":{"date-parts":[[2016,9,23]],"date-time":"2016-09-23T07:54:38Z","timestamp":1474617278000},"page":"1759006","source":"Crossref","is-referenced-by-count":13,"title":["Population Decomposition-Based Greedy Approach Algorithm for the Multi-Objective Knapsack Problems"],"prefix":"10.1142","volume":"31","author":[{"given":"Jiawei","family":"Yuan","sequence":"first","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, P. R. China"}]},{"given":"Hai-Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, P. R. China"}]},{"given":"Chaoda","family":"Peng","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, P. R. 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