A Novel Decomposition-Based Multi-Objective Symbiotic Organism Search Optimization Algorithm

In this research, the effectiveness of a novel optimizer dubbed as decomposition-based multi-objective symbiotic organism search (MOSOS/D) for multi-objective problems was explored. The proposed optimizer was based on the symbiotic organisms’ search (SOS), which is a star-rising metaheuristic inspired by the natural phenomenon of symbioses among living organisms. A decomposition framework was incorporated in SOS for stagnation prevention and its deep performance analysis in real-world applications. The investigation included both qualitative and quantitative analyses of the MOSOS/D metaheuristic. For quantitative analysis, the MOSOS/D was statistically examined by using it to solve the unconstrained DTLZ test suite for real-parameter continuous optimizations. Next, two constrained structural benchmarks for real-world optimization scenario were also tackled. The qualitative analysis was performed based on the characteristics of the Pareto fronts, boxplots, and dimension curves. To check the robustness of the proposed optimizer, comparative analysis was carried out with four state-of-the-art optimizers, viz., MOEA/D, NSGA-II, MOMPA and MOEO, grounded on six widely accepted performance measures. The feasibility test and Friedman’s rank test demonstrates the dominance of MOSOS/D over other compared techniques and exhibited its effectiveness in solving large complex multi-objective problems.

[1]  M. Nadimi-Shahraki,et al.  DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization , 2022, Expert Syst. Appl..

[2]  A. Gandomi,et al.  Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization , 2022, Computer Methods in Applied Mechanics and Engineering.

[3]  S. Mirjalili,et al.  GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems , 2022, J. Comput. Sci..

[4]  Yongquan Zhou,et al.  MOMPA: Multi-objective marine predator algorithm , 2021 .

[5]  Raluca Vernic,et al.  MOSOSS: an adapted multi-objective symbiotic organisms search for scheduling , 2021, Soft Computing.

[6]  Jing Chen,et al.  A multi-objective optimization for resource allocation of emergent demands in cloud computing , 2021, J. Cloud Comput..

[7]  Seniha Ketenci,et al.  Multi-objective symbiotic organism search algorithm for optimal feature selection in brain computer interfaces , 2021, Expert Syst. Appl..

[8]  Nantiwat Pholdee,et al.  Hybrid Heat Transfer Search and Passing Vehicle Search optimizer for multi-objective structural optimization , 2020, Knowl. Based Syst..

[9]  Pradeep Jangir,et al.  Multi-objective equilibrium optimizer: framework and development for solving multi-objective optimization problems , 2021, J. Comput. Des. Eng..

[10]  Pradeep Jangir,et al.  MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems , 2021, IEEE Access.

[11]  Serdar Carbas,et al.  A symbiotic organisms search algorithm-based design optimization of constrained multi-objective engineering design problems , 2020 .

[12]  Tansel Dökeroglu,et al.  A survey on new generation metaheuristic algorithms , 2019, Comput. Ind. Eng..

[13]  Md. Asri Ngadi,et al.  A survey of symbiotic organisms search algorithms and applications , 2019, Neural Computing and Applications.

[14]  Ezugwu E. Absalom,et al.  Symbiotic organisms search algorithm: Theory, recent advances and applications , 2019, Expert Syst. Appl..

[15]  Sumit Kumar,et al.  Modified symbiotic organisms search for structural optimization , 2018, Engineering with Computers.

[16]  Trong Nhan Le,et al.  Opposition multiple objective symbiotic organisms search (OMOSOS) for time, cost, quality and work continuity tradeoff in repetitive projects , 2018, J. Comput. Des. Eng..

[17]  Aderemi Oluyinka Adewumi,et al.  Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem , 2017, Expert Syst. Appl..

[18]  Vivek K. Patel,et al.  Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization , 2016, J. Comput. Des. Eng..

[19]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[20]  S. Fong,et al.  Metaheuristic Algorithms: Optimal Balance of Intensification and Diversification , 2014 .

[21]  Gang Chen,et al.  Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms , 2009, IEEE Transactions on Evolutionary Computation.

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

[23]  Enrique Alba,et al.  The exploration/exploitation tradeoff in dynamic cellular genetic algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[24]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

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

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

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