Genetic local search for multi-objective combinatorial optimization

By Andrzej Jaszkiewicz. European Journal of Operational Research, 2002

595 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/1b3a791f40c9b8654d7713d0f4d8acdc314093ae

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Citations in the corpus

A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling (2015, background): “However, many local search strategies, such as the dynamic weighted aggregation method [5], restricted mating strategy [29] and many others [3], [30], [31] have implicitly take n advantage of the connectedness property to enhance the sear ch efficiency.”
A genetic algorithm approach to multiobjective land use planning (2004, background): “A number of papers have reported favourably on the use of GAs for multi-objective combinatorial problems (see, for example, Fonseca and Fleming [14] and Jaszkiewicz [15]).”
Generalizing Surrogate-Assisted Evolutionary Computation (2010, methodology): “The brief outline of a typical Multi-Objective Memetic Algorithm (MOMA) using weighting (scalarization) technique [58][59][60] is illustrated in Algorithm 3.”
Memetic algorithms and memetic computing optimization: A literature review (2012, background): “A randomized weight update, similar to a random walk local search, is proposed in [192] while a fully random update is presented in [76, 79].”
A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, methodology): “Additionally, there are some other methods, such as the multi-objective genetic local search (MOGLS) [229], the multi-objective Tabu search (MOTS) [230], the multi-objective scatter search (MOSS) [231], ACO [24], PSO [205], ABC [151], FL [37], ANN, AIS, game theory [219], MOICA [157], memetic algori”
Simulated annealing-based multiobjective algorithms and their application for system reliability (2003, methodology): “Another method recently proposed by Jaszkiewicz [35] employs a novel genetic local search for multiple objective combinatorial optimizations.”
Deep Reinforcement Learning for Multiobjective Optimization (2019, methodology): “It is further improved and specialized to solve the MOTSP in [41], which signiﬁcantly outperforms the original MOGLS.”
An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “On one hand, some decomposition-based MOEAs such as dynamic weightbased EA [9], multiobjective genetic local search algorithm [10], [11], cellar MOGA [12], multiple single-objective Pareto sampling algorithm-II [13], MOEA based on decomposition (MOEA/D) [14], and reference vector guided EA (RVEA) [1”
Evolutionary Algorithms for Solving Multi-Objective Problems (2002, methodology): “Compares MOGA (Fonseca and Fleming, 1993), Pareto GA with local search (Pareto GLS), multiple objective multiple start local search (MOMSLS) with random weight vectors, and multiple objective genetic local search (MOGLS) (Jaszkiewicz, 1998) Compares MOGA (Fonseca and Fleming, 1993), the NPGA (Horn e”