WBMOAIS

WBMOAIS: A novel artificial immune system for multiobjective optimization. This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.


References in zbMATH (referenced in 10 articles )

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  1. Qiao, Junfei; Li, Fei; Yang, Shengxiang; Yang, Cuili; Li, Wenjing; Gu, Ke: An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection (2020)
  2. Leung, Chris S. K.; Lau, Henry Y. K.: Multiobjective simulation-based optimization based on artificial immune systems for a distribution center (2018)
  3. Lin, Qiuzhen; Zhu, Qingling; Huang, Peizhi; Chen, Jianyong; Ming, Zhong; Yu, Jianping: A novel hybrid multi-objective immune algorithm with adaptive differential evolution (2015)
  4. Gao, Jiaquan; He, Guixia; Liang, Ronghua; Feng, Zhilin: A quantum-inspired artificial immune system for the multiobjective 0-1 knapsack problem (2014)
  5. Lin, Qiuzhen; Chen, Jianyong: A novel micro-population immune multiobjective optimization algorithm (2013)
  6. Murugesan, R.; Sivasakthi Balan, K.: Positive selection based modified clonal selection algorithm for solving job shop scheduling problem (2012)
  7. Rezvanian, Alireza; Meybodi, Mohammad Reza: Tracking extrema in dynamic environments using a learning automata-based immune algorithm (2011)
  8. Zhang, Zhuhong; Qian, Shuqu: Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems (2011) ioport
  9. Gao, Jiaquan; Wang, Jun: WBMOAIS: A novel artificial immune system for multiobjective optimization (2010)
  10. Lian, Zhigang: A united search particle swarm optimization algorithm for multiobjective scheduling problem (2010)