MOEPO: A novel Multi-objective Emperor Penguin Optimizer for global optimization: Special application in ranking of cloud service providers

Abstract This study introduces the extension of currently developed Emperor Penguin Optimizer (EPO) in terms of multi-objective problems solving capability, which is entitled as Multi-objective Emperor Penguin Optimizer (MOEPO) In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. Here, the roulette-wheel approach is utilized to choose the effective archived solutions by simulating the huddling behaviors of emperor penguins. The proposed algorithm is approved by testing it with twenty-four well-known benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on seven constrained problems of engineering to assess its appropriateness for finding solutions of real world problems. After, that it is validated on cloud computing application and compared between competitor approaches. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms.

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

[2]  Maoguo Gong,et al.  Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection , 2008, Evolutionary Computation.

[3]  Gaurav Dhiman,et al.  An Innovative Approach for Face Recognition Using Raspberry Pi , 2020, Artificial Intelligence Evolution.

[4]  Josep M. Guerrero,et al.  A NEW METHODOLOGY CALLED DICE GAME OPTIMIZER FOR CAPACITOR PLACEMENT IN DISTRIBUTION SYSTEMS , 2020, Electrical Engineering & Electromechanics.

[5]  D. Walton,et al.  Practical approach to optimum gear train design , 1988 .

[6]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[7]  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).

[8]  Gaurav Dhiman,et al.  Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications , 2017, Adv. Eng. Softw..

[9]  Heike Trautmann,et al.  Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results , 2016, Comput. Optim. Appl..

[10]  Vijay Kumar,et al.  KnRVEA: A hybrid evolutionary algorithm based on knee points and reference vector adaptation strategies for many-objective optimization , 2019, Applied Intelligence.

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

[12]  Vijay Kumar,et al.  Multi-objective spotted hyena optimizer: A Multi-objective optimization algorithm for engineering problems , 2018, Knowl. Based Syst..

[13]  Atulya K. Nagar,et al.  A novel algorithm for global optimization: Rat Swarm Optimizer , 2020, Journal of Ambient Intelligence and Humanized Computing.

[14]  Gaurav Dhiman,et al.  MoSSE: a novel hybrid multi-objective meta-heuristic algorithm for engineering design problems , 2020, Soft Comput..

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

[16]  Carlos A. Coello Coello,et al.  Multi-Objective Combinatorial Optimization: Problematic and Context , 2010, Advances in Multi-Objective Nature Inspired Computing.

[17]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[18]  Pritpal Singh,et al.  Uncertainty representation using fuzzy-entropy approach: Special application in remotely sensed high-resolution satellite images (RSHRSIs) , 2018, Appl. Soft Comput..

[19]  Adam Slowik,et al.  A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization , 2020, Engineering with Computers.

[20]  Qingfu Zhang,et al.  An External Archive Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Combinatorial Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[21]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[22]  Gaurav Dhiman,et al.  Deep Convolution Neural Network Approach for Defect Inspection of Textured Surfaces , 2020, Journal of the Institute of Electronics and Computer.

[23]  Jinhua Zheng,et al.  Spread Assessment for Evolutionary Multi-Objective Optimization , 2009, EMO.

[24]  Ganapati Panda,et al.  Solving multiobjective problems using cat swarm optimization , 2012, Expert Syst. Appl..

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

[26]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[27]  Sambit Bakshi,et al.  Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer , 2019, Swarm Evol. Comput..

[28]  Anurag Rai,et al.  An analysis of QoS ranking prediction framework techniques , 2019, Modern Physics Letters B.

[29]  Vijay Kumar,et al.  Spotted Hyena Optimizer for Solving Complex and Non-linear Constrained Engineering Problems , 2018, Harmony Search and Nature Inspired Optimization Algorithms.

[30]  Gaurav Dhiman,et al.  ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems , 2019, Engineering with Computers.

[31]  K. M. Ragsdell,et al.  Optimal Design of a Class of Welded Structures Using Geometric Programming , 1976 .

[32]  Kalyanmoy Deb,et al.  Advances in Evolutionary Multi-objective Optimization , 2012, SSBSE.

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

[34]  Amandeep Kaur,et al.  Spotted Hyena Optimizer for Solving Engineering Design Problems , 2017, 2017 International Conference on Machine Learning and Data Science (MLDS).

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

[36]  Dunwei Gong,et al.  A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems , 2018, IEEE Transactions on Evolutionary Computation.

[37]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[38]  Gaurav Dhiman,et al.  A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants , 2020, Neural Computing and Applications.

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

[40]  Yun Li,et al.  Optimization and robustness for crashworthiness of side impact , 2001 .

[41]  Vijay Kumar,et al.  Emperor penguin optimizer: A bio-inspired algorithm for engineering problems , 2018, Knowl. Based Syst..

[42]  Pritpal Singh,et al.  A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches , 2018, J. Comput. Sci..

[43]  Gaurav Dhiman,et al.  A four-way decision-making system for the Indian summer monsoon rainfall , 2018, Modern Physics Letters B.

[44]  Hadi Givi,et al.  Darts Game Optimizer: A New Optimization Technique Based on Darts Game , 2020 .

[45]  Gaurav Dhiman,et al.  MOSHEPO: a hybrid multi-objective approach to solve economic load dispatch and micro grid problems , 2019, Applied Intelligence.

[46]  Amandeep Kaur,et al.  STOA: A bio-inspired based optimization algorithm for industrial engineering problems , 2019, Eng. Appl. Artif. Intell..

[47]  Vijay Kumar,et al.  Automatic clustering using quantum-based multi-objective emperor penguin optimizer and its applications to image segmentation , 2019, Modern Physics Letters A.

[48]  A. L. Sangal,et al.  Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization , 2020, Eng. Appl. Artif. Intell..

[49]  Amandeep Kaur,et al.  A Review on Search-Based Tools and Techniques to Identify Bad Code Smells in Object-Oriented Systems , 2018, Harmony Search and Nature Inspired Optimization Algorithms.

[50]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored , 2009, Frontiers of Computer Science in China.

[51]  Amandeep Kaur,et al.  A Hybrid Algorithm Based on Particle Swarm and Spotted Hyena Optimizer for Global Optimization , 2018, SocProS.

[52]  Xin-She Yang,et al.  Flower pollination algorithm: A novel approach for multiobjective optimization , 2014, ArXiv.

[53]  Marco Laumanns,et al.  Computing Gap Free Pareto Front Approximations with Stochastic Search Algorithms , 2010, Evolutionary Computation.

[54]  Daniel Angus,et al.  Multiple objective ant colony optimisation , 2009, Swarm Intelligence.

[55]  Kazuyuki Murase,et al.  Evolutionary Path Control Strategy for Solving Many-Objective Optimization Problem , 2015, IEEE Transactions on Cybernetics.

[56]  Ritika Maini,et al.  DHIMAN: A novel algorithm for economic Dispatch problem based on optimization metHod usIng Monte Carlo simulation and Astrophysics coNcepts , 2019, Modern Physics Letters A.

[57]  Mengjie Zhang,et al.  A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[58]  Sen Guo,et al.  ED-SHO: A framework for solving nonlinear economic load power dispatch problem using spotted hyena optimizer , 2018, Modern Physics Letters A.

[59]  Pritpal Singh,et al.  A Fuzzy-LP Approach in Time Series Forecasting , 2017, PReMI.

[60]  R. Venkata Rao,et al.  A new optimization algorithm for solving complex constrained design optimization problems , 2017 .

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

[62]  Vijay Kumar,et al.  Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems , 2019, Knowl. Based Syst..

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

[64]  Carlos A. Coello Coello,et al.  Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[65]  Sen Guo,et al.  A hybrid fuzzy quantum time series and linear programming model: Special application on TAIEX index dataset , 2019, Modern Physics Letters A.

[66]  Gaurav Dhiman,et al.  A quantum approach for time series data based on graph and Schrödinger equations methods , 2018, Modern Physics Letters A.