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Multi- or many-objective evolutionary algorithm- s(MOEAs), especially thedecomposition-based MOEAs have been widely concerned in recent years. Thedecomposition-based MOEAs emphasize convergence and diversity in a simple modeland have made a great success in dealing with theoretical and practical multi-or many-objective optimization problems. In this paper, we focus on updatestrategies of the decomposition- based MOEAs, and their criteria for comparingsolutions. Three disadvantages of the decomposition-based MOEAs with localupdate strategies and several existing criteria for comparing solutions areanalyzed and discussed. And a global loop update strategy and two hybridcriteria are suggested. Subsequently, an evolutionary algorithm with the globalloop update is implement- ed and compared to several of the best multi- ormany-objective optimization algorithms on two famous unconstraint test suiteswith up to 15 objectives. Experimental results demonstrate that unlikeevolutionary algorithms with local update strategies, the population of ouralgorithm does not degenerate at any generation of its evolution, whichguarantees the diversity of the resulting population. In addition, ouralgorithm wins in most instances of the two test suites, indicating that it isvery compet- itive in terms of convergence and diversity. Running results ofour algorithm with different criteria for comparing solutions are alsocompared. Their differences are very significant, indicating that theperformance of our algorithm is affected by the criterion it adopts.

A mullti- or many- objective evolutionary algorithm with global loop update