Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling

By Hisao Ishibuchi, Tadashi Yoshida, Tadahiko Murata. IEEE Transactions on Evolutionary Computation, 2003

793 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/382f4ab5708ea74e001ac7bcfe3a8c7b52c10256

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Citations in the corpus

Hybrid Genetic Algorithms: A Review (2006, methodology): “Local search duration influences the balance between the global exploration of genetic algorithms and local refinement of the neighborhood search method in hybrid genetic algorithms [122], [123].”
Multi-objective optimization using genetic algorithms: A tutorial (2006, methodology): “[56] also used the weighted sum of the objective functions to evaluate solutions during the local search.”
Localized Weighted Sum Method for Many-Objective Optimization (2018, methodology): “In the two-phase local search (TPLS) and double-TPLS [61] and the multi-objective genetic local search [62]–[64] the WS method is also used to guide the”
Accelerating Differential Evolution Using an Adaptive Local Search (2008, background): “optimization, we need to take advantage of both the exploration abilities of EA and the exploitation abilities of LS by combining them in a well-balanced manner [38].”
Backtracking Search Optimization Algorithm for numerical optimization problems (2013, background): “Many EAs are based on basic genetic rules, such as ABC, the cuckoo search, the covariance matrix adaptation evolution strategy (CMAES) [37], genetic algorithms [1,2] and the differential evolution algorithm (DE) [30,34,35].”
An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling (2007, methodology): “In MAs, several studies [21], [ 25 ] have been focused on how to achieve a reasonable combination of global and local searches and how to make a good balance between exploration and exploitation.”
Response surface approximation of pareto optimal front in multi-objective optimization (2004, methodology): “[16] that using a local search method on this approximate Pareto optimal solution set can further improve the solutions and help better converge to the Pareto optimal set.”
Memetic algorithms and memetic computing optimization: A literature review (2012, background): “search on the basis of the principles explained in [78].”
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA (2008, methodology): “Another measure called displacement [8], [24], that reflects both the proximity to and the coverage of the true PO front, is also used here for the purpose of comparison.”
Modified Distance Calculation in Generational Distance and Inverted Generational Distance (2015, methodology): “The IGD indicator with the Euclidean distance was used in [2], [13] in 2003 without referring to it as IGD.”
Classification of adaptive memetic algorithms: a comparative study (2006, background): “It has shown to be successful and popular for solving optimization problems in many contexts [8]–[21].”
Ensemble of Constraint Handling Techniques (2010, methodology): “As ECHT employs different constraint handling methods each having its own population, it can be compared with hybrid methods like memetic algorithms [32]–[34].”