M-PAES: a memetic algorithm for multiobjective optimization

By Joshua D. Knowles, David Wolfe Corne. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2000

404 citations recorded by the source corpus.

A memetic algorithm for tackling multiobjective optimization problems is presented. The algorithm employs the proven local search strategy used in the Pareto archived evolution strategy (PAES) and combines it with the use of a population and recombination. Verification of the new M-PAES (memetic PAES) algorithm is carried out by testing it on a set of multiobjective 0/1 knapsack problems. On each problem instance, a comparison is made between the new memetic algorithm, the (1+1)-PAES local searcher, and the strength Pareto evolutionary algorithm (SPEA) of E. Zitzler and L. Thiele (1998, 1999).

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/2883f7edbe5d4a80bb694a4ee36abce29cab5706

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In Artificial Intelligence

1. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Kay Chen Tan
6. Hisao Ishibuchi
7. Yew-Soon Ong
8. Ferrante Neri

In Computational Theory and Mathematics

1. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Kay Chen Tan
6. Yew-Soon Ong
7. Hisao Ishibuchi
8. Ferrante Neri

Citations in the corpus

• Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms (2003, methodology): “This is the case for the PDE [1], PESA [8], NSGA-II [10], PAES [17], M-PAES [ 18 ], DMOEA [20], SPEA2 [27], SPEA [28], and the maximin MOEA of [2].”
• Multi-objective optimization using genetic algorithms: A tutorial (2006, background): “Knowles and Corne [53] presented a memetic version of PAES, called MPAES.”
• A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, neutral): “Memetic Algorithm (MA) [167–170] Multi-objective MA [314]”
• A tutorial for competent memetic algorithms: model, taxonomy, and design issues (2005, background): “[21]–[24], an extensive bibliography can be found in [25]), and numerical optimisation (see e.”
• The balance between proximity and diversity in multiobjective evolutionary algorithms (2003, methodology): “The current state(cid:150)of(cid:150)the(cid:150)art in multi(cid:150)objective evolutionary optimization is represented by a Pareto set of different MOEAs, which include the NSGA(cid:150)II by Deb et al. [19], the SPEA by Zitzler and Thiele [3], the SPEA(cid:150)II by Zitzler et al. [37], the PAES ”
• Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling (2003, methodology): “Jaszkiewicz's MOGLS and the M-PAES were also compared with each other on multiobjective knapsack problems by Knowles and Corne [24], where better results were obtained from the M-PAES than Jaszkiewicz's MOGLS.”
• A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, neutral): “, NSGA-II [6], SPEA2 [7], IBEA [8], MOEA/D [9], PESA-II [10], and M-PAES [11], just to name a few.”
• An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization (2015, background): “Digital Object Identifier 10.1109/TEVC.2014.2308305 Over the past 20 years, a variety of evolutionary algorithms have been developed to tackle MOPs, for example, Pareto envelop-based selection algorithm II (PESA-II) [1], nondominated sorting genetic algorithm-II (NSGA-II) [2], strength Pareto evolut”
• A Multi-Facet Survey on Memetic Computation (2011, methodology): “In the domain of multiobjective optimization (MOO) [7], [33], [48], the more popular population-based methods include the non-dominated sorting genetic algorithm-II [62], evolutionary multiobjective optimization algorithms [27], Pareto archived evolutionary strategy [63], multiobjective PSO [64], et”
• PESA-II: region-based selection in evolutionary multiobjective optimization (2001, methodology): “…Pareto Evolutionary Algorithm{Zitzler and Thiele, 1999), PAES (Pareto ArchivedEvolution Strategy { Knowles and Corne, 2000), M-PAES (Memetic PAES { Knowles and Corne, 2000a),PESA (Pareto Envelope based Selection { Corne etal, 2000), MOMGA (Multi-Objective Messy GeneticAlgorithm, Van Veldhuizen…”
• Memetic algorithms and memetic computing optimization: A literature review (2012, methodology): “In [92, 93] a greedy local search method based on dominance relation is proposed.”