Multi-objective Firefly Algorithm Guided by Elite Particle

With the diversification and complexity of the social needs, multi-objective optimization problems gradually attract more and more attention. The traditional multi-objective optimization algorithms cannot meet the practical needs. Therefore, it is urgent to improve and develop new multi-objective optimization algorithms to meet the challenges. On the basis of standard firefly algorithm, this paper proposed a multi-objective firefly algorithm based on population evolution guided by elite particle. The algorithm randomly selects a non-inferior solution as the elite particle to participate in the population evolution, extends the detection range of firefly, and improves the diversity and accuracy of the non-inferior solution set. The experimental results show that the proposed algorithm is superior to the MOPSO, MOEA/D, PESA-II, NSGA-III algorithm on the GD, SP, MS and other quantitative indexes for the seven classic test functions, and the proposed algorithm is an effective method for multi-objective optimization.

[1]  Xianpeng Wang,et al.  A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization Problems , 2013, IEEE Transactions on Evolutionary Computation.

[2]  Fei Xue,et al.  Optimal parameter settings for bat algorithm , 2015, Int. J. Bio Inspired Comput..

[3]  Hui Wang,et al.  Firefly algorithm with random attraction , 2016, Int. J. Bio Inspired Comput..

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

[5]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[6]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[7]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[8]  Jia Zhao,et al.  The Firefly Algorithm with Gaussian Disturbance and Local Search , 2018, J. Signal Process. Syst..

[9]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[10]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

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

[12]  Hui Sun,et al.  Artificial bee colony algorithm with improved special centre , 2016, Int. J. Comput. Sci. Math..

[13]  Zheng Xiang.  Progress of Research on Multi-Objective Evolutionary Algorithms , 2007 .

[14]  Hui Wang,et al.  Improved multi-strategy artificial bee colony algorithm , 2016 .

[15]  Adnan Shaout,et al.  Many-Objective Software Remodularization Using NSGA-III , 2015, TSEM.

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

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

[18]  Jun Wang,et al.  Adaptive Intelligent Single Particle Optimizer Based Image De-noising in Shearlet Domain , 2017, Intell. Autom. Soft Comput..

[19]  Xin-She Yang,et al.  A new hybrid method based on krill herd and cuckoo search for global optimisation tasks , 2016, Int. J. Bio Inspired Comput..

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

[21]  Qidi Wu,et al.  Bat algorithm with Gaussian walk , 2014, Int. J. Bio Inspired Comput..

[22]  Ruixiang Li,et al.  Artificial bee colony with bidirectional search , 2017 .

[23]  Zhihua Cui,et al.  Artificial plant optimisation algorithm with three-period photosynthesis , 2013, Int. J. Bio Inspired Comput..

[24]  Yong Wang,et al.  A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization , 2006, IEEE Transactions on Evolutionary Computation.

[25]  Kalyanmoy Deb,et al.  Introducing Robustness in Multi-Objective Optimization , 2006, Evolutionary Computation.

[26]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[27]  Gan Yu.  A new multi-population-based artificial bee colony for numerical optimisation , 2017 .

[28]  Liu Li,et al.  Multi-objective Particle Swarm Optimization Based on Adaptive Grid Algorithms , 2008 .

[29]  Zhihua Cui,et al.  APOA with parabola model for directing orbits of chaotic systems , 2013, Int. J. Bio Inspired Comput..

[30]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

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

[32]  Kalyanmoy Deb,et al.  A dual-population paradigm for evolutionary multiobjective optimization , 2015, Inf. Sci..

[33]  Hui Wang,et al.  Particle Swarm Optimization based on Vector Gaussian Learning , 2017, KSII Trans. Internet Inf. Syst..

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

[35]  T. Pham,et al.  Ovarian Cancer Identification from Mass Spectra by Kernel Fisher Discriminant Analysis , 2007 .

[36]  Xin-She Yang,et al.  Multiobjective firefly algorithm for continuous optimization , 2012, Engineering with Computers.

[37]  Xiao Yu-feng,et al.  Overview on multi-objective optimization problem research , 2011 .

[38]  Hui Wang,et al.  Firefly algorithm with neighborhood attraction , 2017, Inf. Sci..