DMMOGSA: Diversity-enhanced and memory-based multi-objective gravitational search algorithm

Multi-objective optimization (MOO) is an important research topic in both science and engineering. This paper proposes a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA). We combine the memory of the best states of individual particles and their population in their evolution paths and the gravitational rules to construct a new search strategy. Under this strategy, the position and mass states of each particle are updated based on the memory associated with it and the current states of all particles in the current population in terms of their gravitational forces on it. A novel diversity-enhancement mechanism is also employed to control the velocity of each particle for traveling to a new position. Experiments were conducted on 12 well-known benchmark functions, and for each function the results of DMMOGSA were compared with those of SPEA2, NSGA-II and MOPSO. Our results show that DMMOGSA can reduce the effect of premature convergence and achieve more reliable performance on most of the tested cases.

[1]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[2]  Kittipong Boonlong,et al.  Multi-objective genetic algorithms for solving portfolio optimization problems in the electricity market , 2014 .

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

[4]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[5]  Oscar Montiel,et al.  High-Performance Architecture for the Modified NSGA-II , 2013, Soft Computing Applications in Optimization, Control, and Recognition.

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

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

[8]  Mahdi Nikusokhan,et al.  A Multi-Objective Gravitational Search Algorithm Based on Non-Dominated Sorting , 2012, Int. J. Swarm Intell. Res..

[9]  Xiaodong Li,et al.  A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization , 2003, GECCO.

[10]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  YangChao-Lung,et al.  Non-dominated sorting genetic algorithm using fuzzy membership chromosome for categorical data clustering , 2015 .

[12]  R. J. Kuo,et al.  Non-dominated sorting genetic algorithm using fuzzy membership chromosome for categorical data clustering , 2015, Appl. Soft Comput..

[13]  Alex Fraser,et al.  Simulation of Genetic Systems by Automatic Digital Computers I. Introduction , 1957 .

[14]  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.

[15]  Modjtaba Rouhani,et al.  A Multi-objective Gravitational Search Algorithm , 2010, CICSyN.

[16]  Veena Sharma,et al.  Disruption based gravitational search algorithm for short term hydrothermal scheduling , 2015, Expert Syst. Appl..

[17]  J. S. F. Barker,et al.  Simulation of Genetic Systems by Automatic Digital Computers , 1958 .

[18]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[19]  Inés Couso,et al.  Generalizing the Wilcoxon rank-sum test for interval data , 2015, Int. J. Approx. Reason..

[20]  Leandro dos Santos Coelho,et al.  Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization , 2016, Expert Syst. Appl..

[21]  S. Ramesh,et al.  Application of modified NSGA-II algorithm to multi-objective reactive power planning , 2012, Appl. Soft Comput..

[22]  Jonathan E. Fieldsend,et al.  A Multi-Objective Algorithm based upon Particle Swarm Optimisation, an Efficient Data Structure and , 2002 .

[23]  Carlos A. Coello Coello,et al.  A particle swarm optimizer for multi-objective optimization , 2005 .

[24]  M. A. El-Shorbagy,et al.  Local search based hybrid particle swarm optimization algorithm for multiobjective optimization , 2012, Swarm Evol. Comput..

[25]  Yuping Wang,et al.  A new multi-objective particle swarm optimization algorithm based on decomposition , 2015, Inf. Sci..

[26]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[27]  James T. McLeskey,et al.  Multi-objective particle swarm optimization of binary geothermal power plants , 2015 .

[28]  A. Kaveh,et al.  A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization , 2011, Expert Syst. Appl..

[29]  Ajoy Kumar Chakraborty,et al.  Solution of optimal power flow using nondominated sorting multi objective gravitational search algorithm , 2014 .

[30]  Jianqiang Li,et al.  A novel multi-objective particle swarm optimization with multiple search strategies , 2015, Eur. J. Oper. Res..

[31]  Qidi Wu,et al.  Numerical comparisons of migration models for Multi-objective Biogeography-Based Optimization , 2016, Inf. Sci..

[32]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[33]  Fang Liu,et al.  A co-evolutionary multi-objective optimization algorithm based on direction vectors , 2013, Inf. Sci..

[34]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[35]  Russell C. Eberhart,et al.  Recent advances in particle swarm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

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

[37]  Siti Mariyam Hj. Shamsuddin,et al.  CAPSO: Centripetal accelerated particle swarm optimization , 2014, Inf. Sci..

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

[39]  As Fraser,et al.  Simulation of Genetic Systems by Automatic Digital Computers VII. Effects of Reproductive Ra'l'e, and Intensity of Selection, on Genetic Structure , 1960 .

[40]  Zhicheng Ji,et al.  A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints , 2014 .

[41]  Xiang Yu,et al.  Enhanced comprehensive learning particle swarm optimization , 2014, Appl. Math. Comput..

[42]  Bernard F. Schutz.  Gravity from the Ground Up: An Introductory Guide to Gravity and General Relativity , 2003 .

[43]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[44]  J. D. Schaffer,et al.  Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition) , 1984 .

[45]  Juan-Zi Li,et al.  A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure , 2015, Inf. Sci..

[46]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[47]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[48]  Aizhu Zhang,et al.  A Hybrid Genetic Algorithm and Gravitational Search Algorithm for Global Optimization , 2015 .

[49]  Jack L. Crosby,et al.  Computer simulation in genetics. , 1973 .

[50]  Pratyusha Rakshit,et al.  Extending multi-objective differential evolution for optimization in presence of noise , 2015, Inf. Sci..

[51]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).