Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization

By Kay Chen Tan, Tong-heng Lee, Eik Fun Khor. IEEE Transactions on Evolutionary Computation, 2001

296 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/27a45e20a10417a63d742aa4ac125082c5d19fd6

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Citations in the corpus

Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection (2008, background): “Some references have implied that MOEAs with small population size have difﬁculties in converging to the true Pareto-optimal front and obtaining the well-distributed solutions for some complicated problems (Deb, 2001; Tan et al., 2001; Khare et al., 2003).”
Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling (2003, background): “[49] proposed an idea of adjusting the number of solutions examined in local search in their multiobjective memetic algorithm.”
Exploring dynamic self-adaptive populations in differential evolution (2006, background): “Investigations on dynamically adaptive populations have also shown that the approach improved the performance of evolutionary multi-objective optimization (EMO) algorithms [20] as well as particle swarm optimization (PSO) algorithms [15].”
Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons (2001, methodology): “…and Ishibuchi, 1995), HLGA from (Hajela and Lin, 1992), NPGA from (Horn et al., 1994), MOGA from (Fonseca and Fleming, 1993), NSGA from (Srinivas and Deb, 1994), SPEA from (Zitzler and Thiele, 1999), MOEA from (Tan et al., 1999), IMOEA from (Tan et al., 2000) and EMOEA from (Tan et al., 2001).”
Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II (2009, methodology): “7–10 that the ﬁnal solutions obtained by MOEA/D-DE have better spread and convergence than those by NSGA-II-DE on these two instances.”
Multi-objective optimization using genetic algorithms: A tutorial (2006, background): “[57] proposed applying a local search procedure to only solutions that are located apart from others.”
A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization (2009, background): “Many researchers, such as [8], [11], [57], [59], and [70] have applied these test problems to examine their proposed algorithms in static environments.”
Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art (2006, background): “In evolutionarymulti-objectiveoptimizationin general,theuseof self-adaptationor online adaptationmechanismsis scarce(seefor example [63, 1, 7]), andwe areonly awareof onemultiobjectiveevolutionaryalgorithmwhichwasdesigned”
A Multi-Facet Survey on Memetic Computation (2011, background): “While many of these refinement methods have been designed for single-objective optimization, others specifically for multiobjective optimization have also emerged [7], [63], [76], [77].”