Miroslaw Blocho
ABSTRACT
Solving rich vehicle routing problems has become an important research avenue due to a plethora of their practical applications. Such discrete optimization problems commonly deal with multiple aspects of intelligent transportation systems through mapping them into the objectives which should be targeted by the optimization algorithm. In this paper, we introduce the cooperative co-evolutionary memetic algorithm for this task. It benefits from the simultaneous evolution of several subpopulations, each corresponding to a single objective, and from the process of migrating the best individuals across such subpopulations to effectively guide the search process. The experimental study performed over widely-used benchmark test cases indicates that our algorithm significantly outperforms the memetic techniques which tackle each objective separately and those that turn the multi-objective problem into a single-objective one through weighting the optimization criteria.
- Z. AI Chami, H. Manier, M.-A. Manier, and C. Fitouri. 2017. A hybrid genetic algorithm to solve a multi-objective Pickup and Delivery Problem. In IFAC Papers Online, Vol. 50. Elsevier, 14656--14661.Google Scholar
- J. Bader and E. Zitzler. 2011. HypE: An algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19, 1 (2011), 45--76.Google ScholarDigital Library
- M. Blocho and J. Nalepa. 2017. LCS-Based Selective Route Exchange Crossover for the Pickup and Delivery Problem with Time Windows. In Proc. EvoCOP (Lecture Notes in Computer Science, Vol. 10197). 124--140.Google Scholar
- K. Deb and H. Jain. 2014. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Trans. Evol. Comput. 18, 4 (2014), 577--601.Google ScholarCross Ref
- K. Deb, M. Mohan, and S. Mishra. 2005. Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evol. Comput. 13, 4 (2005), 501--525.Google ScholarDigital Library
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 2 (2002), 182--197.Google ScholarDigital Library
- D. Gaul, K. Klamroth, and M. Stiglmay. 2021. Event-based MILP models for ridepooling applications. EJOR (2021). Google ScholarCross Ref
- F. Guerriero, F. Pezzella, O. Pisacane, and L. Trollini. 2014. Multi-objective optimization in dial-a-ride public transportation. In Transp. Res. Procedia, Vol. 3. 299--308.Google ScholarCross Ref
- X. Jin, H. Qin, Z. Zhang, M. Zhou, and J. Wang. 2021. Planning of Garbage Collection Service: An Arc-Routing Problem With Time-Dependent Penalty Cost. IEEE Trans. on Intell. Transp. Syst. 22, 5 (2021), 2692--2705.Google ScholarDigital Library
- M. Köppen, R. Vicente-Garcia, and B. Nickolay. 2005. Fuzzy-Pareto dominance and its application in evolutionary multi-objective optimization. In Proc. EMO. Springer, 399--412.Google Scholar
- A. López-Jaimes, C.C. Coello, and D. Chakraborty. 2008. Objective reduction using a feature selection technique. In Proc. GECCO. 673--680.Google Scholar
- X. Ma, X. Li, Q. Zhang, K. Tang, Z. Liang, W. Xie, and Z. Zhu. 2019. A Survey on Cooperative Co-Evolutionary Algorithms. IEEE Trans. Evol. Comput. 23, 3 (2019), 421--441.Google ScholarCross Ref
- J. Nalepa and M. Blocho. 2015. Co-Operation in the Parallel Memetic Algorithm. Int. J. Parallel Program. 43, 5 (2015), 812--839.Google ScholarDigital Library
- J. Nalepa and M. Blocho. 2017. Adaptive guided ejection search for pickup and delivery with time windows. J. Intell. Fuzzy Syst. 32, 2 (2017), 1547--1559.Google ScholarDigital Library
- M. Ramamoorthy, S. Forrest, and V.R. Syrotiuk. 2021. MA-ABC: a memetic algorithm optimizing attractiveness, balance, and cost for capacitated Arc routing problems. In Proc. GECCO. ACM, 1043--1051.Google Scholar
- H. Wang, L. Jiao, and X. Yao. 2015. Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization. IEEE Trans. Evol. Comput. 19, 4 (2015), 524--541.Google ScholarDigital Library
- Q. Zhang and H. Li. 2007. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11, 6 (2007), 712--731.Google ScholarDigital Library
- E. Zitzler and S. Künzli. 2004. Indicator-based selection in multiobjective search. In Proc. PPSN. Springer, 832--842.Google Scholar
Index Terms
- Cooperative co-evolutionary memetic algorithm for pickup and delivery problem with time windows
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