A multi-objective artificial algae algorithm

Abstract In this study, the authors focus on modification of the artificial algae algorithm (AAA), for multi-objective optimization. Basically, AAA is a population-based optimization algorithm inspired by the behavior of microalgae cells. In this work, a modified AAA with appropriate strategies is proposed for multi-objective Artificial Algae Algorithm (MOAAA) from the first AAA that was initially presented to solve single-objective continuous optimization problems. To the best of our knowledge, the MOAAA is the first modification of the AAA for solving multi-objective problems. Performance of the proposed algorithm is examined on a benchmark set consisting of 36 different multi-objective optimization problems and compared with four different swarm intelligence or evolutionary algorithms that are well-known in literature. The MOAAA is highly successful in solving multi-objective problems, and it has been demonstrated that the MOAAA is an alternative competitive algorithm in multi-objective optimization according to experimental results and comparisons presented in this research topic.

[1]  Ka-Chun Wong,et al.  An adaptive immune-inspired multi-objective algorithm with multiple differential evolution strategies , 2018, Inf. Sci..

[2]  Yu Xue,et al.  A hybrid multi-objective firefly algorithm for big data optimization , 2017, Appl. Soft Comput..

[3]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[4]  Ponnuthurai N. Suganthan,et al.  Evolutionary multiobjective optimization in dynamic environments: A set of novel benchmark functions , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[5]  Haritza Camblong,et al.  Multi-objective cooperative scheduling: An application on smart grids , 2019, Applied Computing and Informatics.

[6]  Sanjay Kadam,et al.  A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling , 2018, Appl. Soft Comput..

[7]  Francisco Luna,et al.  MOCell: A cellular genetic algorithm for multiobjective optimization , 2009 .

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

[9]  Qingfu Zhang,et al.  Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition , 2009 .

[10]  Kuang-rong Hao,et al.  Multi-objective workflow scheduling in cloud system based on cooperative multi-swarm optimization algorithm , 2017, Journal of Central South University.

[11]  Vivek Patel,et al.  Multi-objective optimization of a rotary regenerator using tutorial training and self-learning inspired teaching-learning based optimization algorithm (TS-TLBO) , 2016 .

[12]  Alberto Delgado,et al.  A novel multiobjective optimization algorithm based on bacterial chemotaxis , 2010, Eng. Appl. Artif. Intell..

[13]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[14]  R. Lyndon While,et al.  A review of multiobjective test problems and a scalable test problem toolkit , 2006, IEEE Transactions on Evolutionary Computation.

[15]  Enrique Alba,et al.  The jMetal framework for multi-objective optimization: Design and architecture , 2010, IEEE Congress on Evolutionary Computation.

[16]  Jouni Lampinen,et al.  Performance assessment of Generalized Differential Evolution 3 with a given set of constrained multi-objective test problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[17]  Charles Gide,et al.  Cours d'économie politique , 1911 .

[18]  Lizhong Shen,et al.  Multi-objective optimization of cooling galleries inside pistons of a diesel engine , 2018 .

[19]  El-Ghazali Talbi,et al.  A generic fuzzy approach for multi-objective optimization under uncertainty , 2018, Swarm Evol. Comput..

[20]  Jing Liu,et al.  A multi-objective memetic algorithm based on decomposition for big optimization problems , 2016, Memetic Comput..

[21]  Ioan-Daniel Borlea,et al.  Stable Takagi-Sugeno Fuzzy Control Designed by Optimization , 2017 .

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

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

[24]  Ali Sadollah,et al.  Water cycle algorithm for solving constrained multi-objective optimization problems , 2015, Appl. Soft Comput..

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

[26]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[27]  Ahmet zk,et al.  A novel metaheuristic for multi-objective optimization problems , 2017 .

[28]  Mohammad Ali Ahmadi,et al.  Multi objective optimization of performance of three-heat-source irreversible refrigerators based algorithm NSGAII , 2016 .

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

[30]  Carlos A. Coello Coello,et al.  Advances in Multi-Objective Nature Inspired Computing , 2010, Advances in Multi-Objective Nature Inspired Computing.

[31]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[32]  Radu-Emil Precup,et al.  Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems , 2015, Appl. Soft Comput..

[33]  Chang Wook Ahn,et al.  A multi-objective evolutionary approach to automatic melody generation , 2017, Expert Syst. Appl..

[34]  Mohammad Reza Meybodi,et al.  Cellular teaching-learning-based optimization approach for dynamic multi-objective problems , 2018, Knowl. Based Syst..

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

[36]  Christian Fonteix,et al.  Multicriteria optimization using a genetic algorithm for determining a Pareto set , 1996, Int. J. Syst. Sci..

[37]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[38]  Enrique Alba,et al.  SMPSO: A new PSO-based metaheuristic for multi-objective optimization , 2009, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM).

[39]  Ziyan Wu,et al.  A multi-objective tabu search algorithm based on decomposition for multi-objective unconstrained binary quadratic programming problem , 2018, Knowl. Based Syst..

[40]  S. Suresh,et al.  BMOBench: Black-Box Multi-Objective Optimization Benchmarking Platform , 2016 .

[41]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[42]  Bernhard Sendhoff,et al.  On Test Functions for Evolutionary Multi-objective Optimization , 2004, PPSN.

[43]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[44]  Mohammad Ali Ahmadi,et al.  Thermodynamic and thermo-economic analysis and optimization of an irreversible regenerative closed Brayton cycle , 2015 .

[45]  R. K. Jena,et al.  Task scheduling in cloud environment: A multi-objective ABC framework , 2017 .

[46]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[48]  Carlos A. Coello Coello,et al.  Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance , 2005, EMO.

[49]  Smith Eiamsa-ard,et al.  Pareto based multi-objective optimization of turbulent heat transfer flow in helically corrugated tubes , 2016 .

[50]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[51]  Tahir Sağ,et al.  A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization) , 2016 .

[52]  Gülay Tezel,et al.  Artificial algae algorithm (AAA) for nonlinear global optimization , 2015, Appl. Soft Comput..

[53]  Jay Prakash,et al.  NSABC: Non-dominated sorting based multi-objective artificial bee colony algorithm and its application in data clustering , 2016, Neurocomputing.

[54]  Magdalene Marinaki,et al.  Non-dominated sorting differential evolution algorithm for the minimization of route based fuel consumption multiobjective vehicle routing problems , 2017 .

[55]  Antonio J. Nebro,et al.  jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..

[56]  Yuren Zhou,et al.  An elitism based multi-objective artificial bee colony algorithm , 2015, Eur. J. Oper. Res..

[57]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[58]  Julian Togelius,et al.  MetaCompose: A Compositional Evolutionary Music Composer , 2016, EvoMUSART.

[59]  Reza Akbari,et al.  A multi-objective Artificial Bee Colony for optimizing multi-objective problems , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).

[60]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .