MOSOA: A new multi-objective seagull optimization algorithm

Abstract This study introduces the extension of currently developed Seagull Optimization Algorithm (SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull Optimization Algorithm (MOSOA). In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. The roulette wheel selection approach is utilized to choose the effective archived solutions by simulating the migration and attacking behaviors of seagulls. The proposed algorithm is approved by testing it with twenty-four benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on six constrained problems of engineering design to assess its appropriateness for finding the solutions of real-world problems. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms. The proposed algorithm also considers those Pareto optimal solutions, which demonstrate high convergence.

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

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

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

[4]  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.

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

[6]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[7]  Alice Richardson,et al.  Nonparametric Statistics for Non‐Statisticians: A Step‐by‐Step Approach by Gregory W. Corder, Dale I. Foreman , 2010 .

[8]  Xin-She Yang,et al.  Engineering Optimization: An Introduction with Metaheuristic Applications , 2010 .

[9]  Yu Xue,et al.  A self-adaptive artificial bee colony algorithm based on global best for global optimization , 2017, Soft Computing.

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

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

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

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

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

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

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

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

[18]  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.

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

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

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

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

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

[24]  Ritika Maini,et al.  Impacts of Artificial Intelligence on real-life problems , 2018 .

[25]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

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

[27]  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).

[28]  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.

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

[30]  Dun-Wei Gong,et al.  Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer , 2011, Expert Syst. Appl..

[31]  Farid Najafi,et al.  PSOSCALF: A new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems , 2018, Appl. Soft Comput..

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

[33]  BOSA: Binary Orientation Search Algorithm , 2019, International Journal of Innovative Technology and Exploring Engineering.

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

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

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

[37]  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.

[38]  Guan-Chun Luh,et al.  Multi-objective optimal design of truss structure with immune algorithm , 2004 .

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

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

[41]  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.

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

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

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

[45]  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.

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

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

[48]  Amandeep Kaur,et al.  Design of a novel energy efficient routing framework for Wireless Nanosensor Networks , 2018, 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC).

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

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

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

[52]  J. D. Hoyo,et al.  Handbook of the Birds of the World , 2010 .

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

[54]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

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

[56]  Bin Wu,et al.  Hybrid harmony search and artificial bee colony algorithm for global optimization problems , 2012, Comput. Math. Appl..

[57]  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.

[58]  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.

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

[60]  Rajesh Kumar Chandrawat,et al.  An Analysis of Modeling and Optimization Production Cost Through Fuzzy Linear Programming Problem with Symmetric and Right Angle Triangular Fuzzy Number , 2016, SocProS.

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

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

[63]  Pj Clarkson,et al.  Biobjective Design Optimization for Axial Compressors Using Tabu Search , 2008 .

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

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

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

[67]  Samia Nefti-Meziani,et al.  A Comprehensive Review of Swarm Optimization Algorithms , 2015, PloS one.