A Novel Coupling Algorithm Based on Glowworm Swarm Optimization and Bacterial Foraging Algorithm for Solving Multi-Objective Optimization Problems

In the real word, optimization problems in multi-objective optimization (MOP) and dynamic optimization can be seen everywhere. During the last decade, among various swarm intelligence algorithms for multi-objective optimization problems, glowworm swarm optimization (GSO) and bacterial foraging algorithm (BFO) have attracted increasing attention from scholars. Although many scholars have proposed improvement strategies for GSO and BFO to keep a good balance between convergence and diversity, there are still many problems to be solved carefully. In this paper, a new coupling algorithm based on GSO and BFO (MGSOBFO) is proposed for solving dynamic multi-objective optimization problems (dMOP). MGSOBFO is proposed to achieve a good balance between exploration and exploitation by dividing into two parts. Part I is in charge of exploitation by GSO and Part II is in charge of exploration by BFO. At the same time, the simulation binary crossover (SBX) and polynomial mutation are introduced into the MGSOBFO to enhance the convergence and diversity ability of the algorithm. In order to show the excellent performance of the algorithm, we experimentally compare MGSOBFO with three algorithms on the benchmark function. The results suggests that such a coupling algorithm has good performance and outperforms other algorithms which deal with dMOP.

[1]  Qin Zhang,et al.  A best-path-updating information-guided ant colony optimization algorithm , 2018, Inf. Sci..

[2]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[3]  Xiaodong Li,et al.  A new performance metric for user-preference based multi-objective evolutionary algorithms , 2013, 2013 IEEE Congress on Evolutionary Computation.

[4]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[5]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .

[6]  Zhen Zhao,et al.  The pareto optimal control of inverter based on multi-objective immune algorithm , 2015, 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia).

[7]  Xizhao Wang,et al.  Discrete differential evolutions for the discounted {0-1} knapsack problem , 2017, Int. J. Bio Inspired Comput..

[8]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[9]  Jinjun Chen,et al.  Hybrid multi-objective cuckoo search with dynamical local search , 2017, Memetic Computing.

[10]  Xin Yao,et al.  Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization , 2009, Memetic Comput..

[11]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[12]  Zhijian Wu,et al.  Multi-strategy ensemble artificial bee colony algorithm , 2014, Inf. Sci..

[13]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[14]  Jinjun Chen,et al.  Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things , 2019, J. Parallel Distributed Comput..

[15]  Xingjuan Cai,et al.  NSGA-II with local perturbation , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).

[16]  Pablo Cortés,et al.  A discrete particle swarm optimisation algorithm to operate distributed energy generation networks efficiently , 2018, Int. J. Bio Inspired Comput..

[17]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[18]  Baocai Yin,et al.  Bacterial foraging optimization using novel chemotaxis and conjugation strategies , 2016, Inf. Sci..

[19]  Jing Zhou,et al.  Hybrid glowworm swarm optimization for task scheduling in the cloud environment , 2017 .

[20]  Michael P. Fourman,et al.  Compaction of Symbolic Layout Using Genetic Algorithms , 1985, ICGA.

[21]  P. Suganthan,et al.  Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods , 2011 .

[22]  Teresa Bernarda Ludermir,et al.  Many Objective Particle Swarm Optimization , 2016, Inf. Sci..

[23]  Gan Yu,et al.  Improving firefly algorithm using hybrid strategies , 2018, Int. J. Comput. Sci. Math..

[24]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[25]  Zhihua Cui,et al.  Bat algorithm with principal component analysis , 2018, International Journal of Machine Learning and Cybernetics.

[26]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[27]  Zhihua Cui,et al.  Bat algorithm with triangle-flipping strategy for numerical optimization , 2017, International Journal of Machine Learning and Cybernetics.

[28]  Jason R. Schott Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[29]  Yu Xue,et al.  Improved bat algorithm with optimal forage strategy and random disturbance strategy , 2016, Int. J. Bio Inspired Comput..

[30]  Jinjun Chen,et al.  Special Focus on Pigeon-Inspired Optimization A pigeon-inspired optimization algorithm for many-objective optimization problems , 2019 .

[31]  Yu Xue,et al.  A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems , 2017, J. Parallel Distributed Comput..

[32]  Yongquan Zhou,et al.  An improved cuckoo search algorithm for integer programming problems , 2018, Int. J. Comput. Sci. Math..

[33]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[34]  P C Quan,et al.  "B-cell" mitogenicity of carragheenan in mouse. , 1978, Cellular immunology.

[35]  Voratas Kachitvichyanukul,et al.  A two-level particle swarm optimisation algorithm for open-shop scheduling problem , 2016, Int. J. Comput. Sci. Math..