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• W4220757788 endingPage "108760" @default.
• W4220757788 startingPage "108760" @default.
• W4220757788 abstract "This paper presents a new fitness evaluation approach based on aggregated pairwise comparisons (APC), i.e., a multiplicative maximin fitness ranking indicator with norm- p ( M2F-p ), for solving multi/many-objective problems. The M2F-p uses an adjustable aggregation of pairwise comparisons induced by p to alleviate the incomparability of solutions in terms of Pareto dominance when the number of objectives increases. We analyze the search ability of M2F-p under different p values. It is shown that the p values can control the shape of contour lines (i.e., a set of equal M2F-p values), which can affect the convergence and uniformity of solutions. Then, we illustrate that the M2F-p offers a set of promising properties that can enhance the discriminability of solutions. Further, we develop an efficient algorithm based on M2F-p by using an adaptive p -selection strategy and a diversity-maintenance mechanism. We conduct experiments on a suit of test problems with up to 10 objectives. The experimental results validate the effectiveness of the proposed algorithm on both multi-objective problems and many-objective problems. • We propose M2F-p with adjustable contour lines and analyze its properties. The p value can adjust the shape of the contour lines (i.e., a set of equal M2F-p values). • Based on M2F-p , an efficient algorithm called M2FMOEA is designed. In detail, an adaptive strategy is devised to select p during the search and a new diversity-based method is used in environmental selection. • The effectiveness and efficiency of M2F-p are verified experimentally. Extensive comparisons and discussions are conducted to show that the algorithm obtains a better performance than compared algorithms." @default.
• W4220757788 created "2022-04-03" @default.
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• W4220757788 date "2022-03-01" @default.
• W4220757788 modified "2023-09-24" @default.
• W4220757788 title "A multiplicative maximin-based evaluation approach for evolutionary many-objective optimization" @default.
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• W4220757788 doi "https://doi.org/10.1016/j.asoc.2022.108760" @default.
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