Abstract
In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that variables in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point and uniform crossovers, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators which Controls the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEA ϵ + and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with \(n=\{100,250,500,750,\mbox{1,000}\}\) items (bits) and m = {2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for m ≥ 4 objectives and that their effectiveness becomes larger as the number of objectives m increases. In addition, the contribution of CCG and mutation operators for the solutions search is analyzed and discussed.
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References
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons (2001)
Hughes, E.J.: Evolutionary many-objective optimisation: many once or one many? In: Proc. of 2005 IEEE Congress on Evolutionary Computation (CEC2005), pp. 222–227 (2005)
Aguirre, H., Tanaka, K.: Working principles, behavior, and performance of MOEAs on MNK-landscapes. Eur. J. Oper. Res. 181(3), 1670–1690 (2007)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-Report, No.103 (2001)
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: a short review. In: Proc. of 2008 IEEE Congress on Evolutionary Computation (CEC2008), pp. 2424–2431 (2008)
Sato, H., Aguirre, H., Tanaka, K.: Self-controlling dominance area of solutions in evolutionary many-objective optimization. In: Proc. of 8th Intl. Conf. on Simulated Evolution and Learning (SEAL2010). LNCS, vol. 6457, pp. 455–465 (2010)
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Iterative approach to indicator-based multiobjective optimization. In: Proc. of 2007 IEEE Congress on Evolutionary Computation (CEC2007), pp. 3697–3704 (2007)
Zitzler, E., Kunzili, S.: Indicator-based selection in multiobjective search. In: Proc. of 8th Intl. Conf. on Parallel Problem Solving from Nature (PPSN-VIII). LNCS, vol. 3242, pp. 832–842 (2004)
Kukkonen, S., Lampinen, J.: Ranking-dominance and many-objective optimization. In: Proc. of 2007 IEEE Congress on Evolutionary Computation (CEC2007), pp. 3983–3990 (2007)
Fabre, M., Pulido, G., Coello, C.: Alternative fitness assignment methods for many-objective optimization problems. In: Proc. of 9th International Conference Evolution Artificielle (EA2009). LNCS, vol. 5975, pp. 146–157 (2010)
Sato, H., Aguirre, H., Tanaka, K.: Pareto partial dominance MOEA and hybrid archiving strategy included CDAS in many-objective optimization. In: Proc. of 2010 IEEE Congress on Evolutionary Computation (CEC2010), pp. 3720–3727 (2010)
Ishibuchi, H., Nojima, Y.: Optimization of scalarizing functions through evolutionary multiobjective optimization. In: Proc. of 4th Intl. Conf. on Evolutionary Multi-Criterion Optimization (EMO2007), pp. 51–65 (2007)
Hughes, E.J.: Many-objective directed evolutionary line search. In: Proc. of 2011 Genetic and Evolutionary Computation Conference (GECCO2011), pp. 761–768. ACM Press (2011)
Wagner, T., Beume, N., Naujoks, B.: Pareto-, aggregation- and indicator-based methods in many-objective optimization. In: Proc. of 4th Intl. Conf. on Evolutionary Multi-Criterion Optimization (EMO2007), pp. 742–756 (2007)
Deb, K., Saxena, K.: Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In: Proc. of 2006 IEEE Congress on Evolutionary Computation (CEC2006), pp. 3353–3360 (2006)
Lopez Jaimes, A., Coello, C., Chakraborty, D.: Objective reduction using a feature selection technique. In: Proc. of 2008 Genetic and Evolutionary Computation Conference (GECCO2008), pp. 674–680. ACM Press (2008)
Brockhoff, D., Zitzler, E.: Objective reduction in evolutionary multiobjective optimization: theory and applications. Evol. Comput. 17(2), 135–166 (2009)
Deb, K., Sundar, J.: Preference point based multi-objective optimization using evolutionary algorithms. In: Proc. of 2006 Genetic and Evolutionary Computation Conference (GECCO2006), pp. 635–642 (2006)
Lopez Jaimes, A., Montano, A., Coello, C.: Preference incorporation to solve many-objective airfoil design problems. In: Proc. of 2011 IEEE Congress on Evolutionary Computation (CEC2011), pp. 1605–1612 (2011)
Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Articulating user preferences in many-objective problems by sampling the weighted hypervolume. In: Proc. of 2009 Genetic and Evolutionary Computation Conference (GECCO2009), pp. 555–562. ACM Press (2009)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)
Bentley, J.L., Kung, H.T., Schkolnick, M., Thompson, C.D.: On the average number of maxima in a set of vectors and applications. J. ACM 25(4), 536–543 (1978)
Watanabe, S., Hiroyasu, T., Miki, M.: Neighborhood cultivation genetic algorithm for multi-objective optimization problems. In: Proc. of Genetic and Evolutionary Computation Conference (GECCO2002), pp. 458–465 (2002)
Sato, H., Aguirre, H., Tanaka, K.: Local dominance and local recombination in MOEAs on 0/1 multiobjective knapsack problems. Eur. J. Oper. Res. 181(3), 1670–1690 (2007)
Ishibuchi, H., Shibata, Y.: Mating scheme for controlling the diversity-convergence balance for multiobjective optimization. In: Proc. of 2004 Genetic and Evolutionary Computation Conference (GECCO2004), pp. 1259–1271 (2004)
Zhang, Q., Li, H.: MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Syswerda, G.: Uniform crossover in genetic algorithms. In: Proc. of the 3rd International Conference on Genetic Algorithms (ICGA89), pp. 2–9 (1989)
Spears, W., De Jong, K.A.: An Analysis of Multi-Point Crossover. Foundations of Genetic Algorithms (1990)
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. Ph.D. thesis, Swiss Federal Institute of Technology, Zurich (1999)
Kowatari, N., Oyama, A., Aguirre, H., Tanaka, K.: A study on large population MOEA using adaptive ϵ-box dominance and neighborhood recombination for many-objective optimization. In: Proc. of 6th Learning and Intelligent Optimization Conference (LION6) (2012, in USB-memory)
Fonseca, C., Paquete, L., López-Ibáñez M.: An improved dimension-sweep algorithm for the hypervolume indicator. In: Proc. of 2006 IEEE Congress on Evolutionary Computation (CEC2006), pp. 1157–1163 (2006)
Sato, M., Aguirre, H., Tanaka, K.: Effects of δ-similar elimination and controlled elitism in the NSGA-II multiobjective evolutionary algorithm. In: Proc. of 2006 IEEE Congress on Evolutionary Computation (CEC2006), pp. 3980–398 (2006)
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Sato, H., Aguirre, H. & Tanaka, K. Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems. Ann Math Artif Intell 68, 197–224 (2013). https://doi.org/10.1007/s10472-012-9293-y
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DOI: https://doi.org/10.1007/s10472-012-9293-y
Keywords
- Multiobjective evolutionary algorithms
- Many-objective optimization
- Many-objective 0/1 knapsack problem
- Local recombination
- Controlling the number of crossed genes