Soft Computing

, Volume 21, Issue 9, pp 2215–2236 | Cite as

An adaptive memetic framework for multi-objective combinatorial optimization problems: studies on software next release and travelling salesman problems

  • Xinye Cai
  • Xin Cheng
  • Zhun Fan
  • Erik Goodman
  • Lisong Wang
Methodologies and Application


In this paper, we propose two multi-objective memetic algorithms (MOMAs) using two different adaptive mechanisms to address combinatorial optimization problems (COPs). One mechanism adaptively selects solutions for local search based on the solutions’ convergence toward the Pareto front. The second adaptive mechanism uses the convergence and diversity information of an external set (dominance archive), to guide the selection of promising solutions for local search. In addition, simulated annealing is integrated in this framework as the local refinement process. The multi-objective memetic algorithms with the two adaptive schemes (called uMOMA-SA and aMOMA-SA) are tested on two COPs and compared with some well-known multi-objective evolutionary algorithms. Experimental results suggest that uMOMA-SA and aMOMA-SA outperform the other algorithms with which they are compared. The effects of the two adaptive mechanisms are also investigated in the paper. In addition, uMOMA-SA and aMOMA-SA are compared with three single-objective and three multi-objective optimization approaches on software next release problems using real instances mined from bug repositories (Xuan et al. IEEE Trans Softw Eng 38(5):1195–1212, 2012). The results show that these multi-objective optimization approaches perform better than these single-objective ones, in general, and that aMOMA-SA has the best performance among all the approaches compared.


Multi-objective combinatorial optimization Memetic algorithms Decomposition-based method Local search Adaptation 



This work was supported in part by the National Basic Research Program (also called 973 Program) of China under Grant 2014CB744903, by the National Natural Science Foundation of China (NSFC) under Grant 61300159 and 61175073, by the Natural Science Foundation of Jiangsu Province under Grant BK20130808, by China Postdoctoral Science Foundation under Grant 2015M571751 and by the Fundamental Research Funds for the Central Universities of China under Grant NZ2013306.

Compliance with ethical standards

Conflict of interest

The author(s) of this publication has research support from Nanjing University of Aeronautics and Astronautics. The terms of this arrangement have been reviewed and approved by the university in accordance with its policy on objectivity in research.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Xinye Cai
    • 1
    • 2
  • Xin Cheng
    • 2
  • Zhun Fan
    • 3
  • Erik Goodman
    • 4
  • Lisong Wang
    • 2
  1. 1.The 28th Research Institute of China Electronic Technology Group CorporationNanjingPeople’s Republic of China
  2. 2.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  3. 3.Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques, Department of Electronic Engineering, School of EngineeringShantou UniversityShantouPeople’s Republic of China
  4. 4.BEACON Center for the Study of Evolution in ActionMichigan State UniversityEast LansingUSA

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