MOPOA: A New Multi-Objective Pufferfish Optimization Algorithm
References
- Abdullah J. M., Rashid T. A., Maaroof B. B., Mirjalili S., “Multi-objective fitness-dependent optimizer algorithm,” Neural Computing and Applications, vol. 35, no. 16, pp. 11969-11987, 2023/06/01 2023.
- Ahmed N., Ali Y., Rashid T., “Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms,” Algorithms, vol. 17, p. 416, 09/19 2024.
- Al-Baik O. et al., “Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems,” Biomimetics, vol. 9, no. 2. doi: 10.3390/biomimetics9020065
- Auger A., Bader J., Brockhoff D., Zitzler E., Theory of the Hypervolume Indicator: Optimal μ-Distributions and the Choice of the Reference Point. 2009.
- Bergh F. v. d., Engelbrecht A. P., “A Study of Particle Swarm Optimization Particle Trajectories,” Information Sciences, vol. 176, no. 8, pp. 937–971, 2006.
- Bilgaiyan S., Sagnika S., Das M., “A multi-objective cat swarm optimization algorithm for workflow scheduling in cloud computing environment,” in Intelligent Computing, Communication and Devices: Springer, 2015, pp. 73-84.
- Brockhoff D., Friedrich T., Neumann F., “Analyzing Hypervolume Indicator Based Algorithms,” in Parallel Problem Solving from Nature – PPSN X, Berlin, Heidelberg, 2008, pp. 651-660: Springer Berlin Heidelberg.
- Chen Z., Liu Y., “Individuals redistribution based on differential evolution for covariance matrix adaptation evolution strategy,” Scientific Reports, vol. 12, no. 1, p. 986, 2022/01/19 2022.
- Coello C. A. C., Pulido G. T., Lechuga M. S., “Handling multiple objectives with particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256-279, 2004.
- Das M., Roy A., Maity S., Kar S., Sengupta S., “Solving fuzzy dynamic ship routing and scheduling problem through new genetic algorithm,” Decision Making: Applications in Management and Engineering, vol. 5, pp. 329-361, 10/15 2022.
- Deb K., “Advances in Evolutionary Multi-objective Optimization,” in Search Based Software Engineering, Berlin, Heidelberg, 2012, pp. 1-26: Springer Berlin Heidelberg.
- Deb K., Jain H., “An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints,” IEEE Trans. Evolutionary Computation, vol. 18, no. 4, pp. 577-601, 2014.
- Deb K., Pratap A., Agarwal S., Meyarivan T., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE transactions on evolutionary computation, vol. 6, no. 2, pp. 182-197, 2002.
- Edgeworth F. Y., Mathematical physics; an essay on the application of mathematics to the moral sciences. Kegan Paul, 1881.[New York, AM Kelley, 1961.
- Goldberg D. E., Genetic Algorithms in Search, Optimisation and Machine Learning, 1 ed. USA: Addison-Wesley, 1989.
- Hancer E., Xue B., Zhang M., Karaboga D., Akay B., “A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information,” in 2015 IEEE congress on evolutionary computation (CEC), 2015, pp. 2420-2427: IEEE.
- Hemmatian H., Abdolhossein F., Assareh E., “Optimization of hybrid laminated composites using the multi-objective gravitational search algorithm (MOGSA),” Engineering Optimization, vol. 46, no. 9, pp. 1169-1182, 2014/09/02 2014.
- Hou J., Du J., Chen Z., “Time-Optimal Trajectory Planning for the Manipulator Based on Improved Non-Dominated Sorting Genetic Algorithm II,” Applied Sciences, vol. 13, p. 6757, 06/01 2023.
- Igel C., Hansen N., Roth S., “Covariance Matrix Adaptation for Multi-objective Optimization,” Evolutionary computation, vol. 15, pp. 1-28, 02/01 2007.
- Ishibuchi H., Imada R., Setoguchi Y., Nojima Y., Performance comparison of NSGAII and NSGA-III on various many-objective test problems. 2016, pp. 3045-3052.
- Ishibuchi H., Tsukamoto N., Sakane Y., Nojima Y., Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions. 2010.
- Jiang M., Qiu L., Huang Z., Yen G. G., “Dynamic Multi-objective Estimation of Distribution Algorithm based on Domain Adaptation and Nonparametric Estimation,” Information Sciences, vol. 435, pp. 203-223, 2018/04/01/ 2018.
- Kim I. Y., de Weck O., “Adaptive weighted sum method for multiobjective optimization: A new method for Pareto front generation,” Structural and Multidisciplinary Optimization, vol. 31, pp. 105-116, 02/01 2006.
- Knowles J., Corne D., “Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy,” Evolutionary Computation, vol. 8, 01/17 2000.
- Laumanns M., Thiele L., Deb K., Zitzler E., “Combining Convergence and Diversity in Evolutionary Multi-Objective Optimization,” Evolutionary computation, vol. 10, pp. 263-82, 02/01 2002.
- Liang J., Qu B., Gong D., Yue C., Problem Definitions and Evaluation Criteria for the CEC 2019 Special Session on Multimodal Multiobjective Optimization. 2019.
- Lin W. et al., “Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations,” Engineering Optimization, vol. 47, no. 7, pp. 994-1007, 2015/07/03 2015.
- Mashwani W., Salhi A., Jan M. A., Sulaiman M., Khanum R., Algarni A., “Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey,” International Journal of Advanced Computer Science and Applications, vol. 7, 03/01 2016.
- Messac A., Mattson C., “Generating Well-Distributed Sets of Pareto Points for Engineering Design Using Physical Programming,” Optimization and Engineering, vol. 3, pp. 431-450, 12/01 2002.
- Mirjalili S., “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Computing and Applications, vol. 27, no. 4, pp. 1053-1073, 2016/05/01 2016.
- Mirjalili S., “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowledge-based systems, vol. 89, pp. 228-249, 2015.
- Mirjalili S., Jangir P., Saremi S., “Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems,” Applied Intelligence, vol. 46, no. 1, pp. 79-95, 2017/01/01 2017.
- Mirjalili S., Lewis A., “The whale optimization algorithm,” Advances in Engineering Software, vol. 95, pp. 51-67, 2016.
- Mirjalili S., Saremi S., Mirjalili S. M., Coelho L. d. S., “Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization,” Expert Systems with Applications, vol. 47, pp. 106-119, 2016.
- Mostaghim S., Jr T., Strategies for finding local guides in multi-objective particle swarm optimization (MOPSO). 2003, pp. 26-33.
- Mu C., Zhang J., Liu Y., Qu R., Huang T., “Multi-objective ant colony optimization algorithm based on decomposition for community detection in complex networks,” Soft Computing, vol. 23, 12/01 2019.
- Negi G., Kumar A., Pant S., Ram M., “Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer,” 2021.
- Pareto V., Cours d’économie politique. Librairie Droz, 1964.
- Parsopoulos K. E., Vrahatis M. N., “Particle swarm optimization method in multiobjective problems,” presented at the Proceedings of the 2002 ACM symposium on Applied computing, Madrid, Spain, 2002. Available: https://doi.org/10.1145/508791.508907
- Pradhan P. M., Panda G., “Solving multiobjective problems using cat swarm optimization,” Expert Systems with Applications, vol. 39, no. 3, pp. 2956-2964, 2012/02/15/ 2012.
- Rahman C. M., Rashid T. A., Ahmed A. M., Mirjalili S., “Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems,” Neural Computing and Applications, vol. 34, no. 8, pp. 6307-6329, 2022/04/01 2022.
- Rashed N. A., Ali Y. H., Rashid T. A., Salih A., “Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems,” p. arXiv:2407.08754Accessed on: June 01, 2024. doi: 10.48550/arXiv.2407.08754 Available: https://ui.adsabs.harvard.edu/abs/2024arXiv240708754R
- Reyes-Sierra M., Coello C., “Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art,” International Journal of Computational Intelligence Research, vol. 2, pp. 287-308, 01/01 2006.
- Santander-Jiménez S., Vega-Rodríguez M. A., “Performance evaluation of dominance-based and indicator-based multiobjective approaches for phylogenetic inference,” Information Sciences, vol. 330, 10/01 2015.
- Sheng W., Liu Y., Meng X., Zhang T., “An Improved Strength Pareto Evolutionary Algorithm 2 with application to the optimization of distributed generations,” Computers & Mathematics with Applications, vol. 64, no. 5, pp. 944-955, 2012/09/01/ 2012.
- Shi Y., Eberhart R. C., “A Modified Particle Swarm Optimizer,” in the IEEE Congress on Evolutionary Computation, 1998, pp. 69–73.
- Sierra M., Coello C., Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. 2005.
- Srinivas N., Deb K., “Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221-248, 1994.
- Stewart R., Palmer T. S., “UTILIZING A REDUCED-ORDER MODEL AND PHYSICAL PROGRAMMING FOR PRELIMINARY REACTOR DESIGN OPTIMIZATION,” EPJ Web Conf., 10.1051/epjconf/202124706049 vol. 247, // 2021.
- Tamaki H., Kita H., Kobayashi S., “Multi-objective optimization by genetic algorithms: a review,” in Proceedings of IEEE International Conference on Evolutionary Computation, 1996, pp. 517-522.
- Toscano Pulido G., Coello C., Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer. 2004, pp. 225-237.
- Velazquez J. M. O., Coello C. A. C., Arias-Montano A., “Multi-objective compact differential evolution,” in 2014 IEEE Symposium on Differential Evolution (SDE), 2014, pp. 1-8.
- Wang B.-C., Li H.-X., Zhang Q., Wang Y., “Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. PP, pp. 1-14, 11/15 2018.
- Xue F., Sanderson A. C., Graves R. J., “Multi-objective differential evolution - algorithm, convergence analysis, and applications,” in 2005 IEEE Congress on Evolutionary Computation, 2005, vol. 1, pp. 743-750 Vol.1.
- Xue Y., Cai X., Neri F., “A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification,” Applied Soft Computing, vol. 127, p. 109420, 2022/09/01/ 2022.
- Yue C., Qu B., Liang J., “A Multi-objective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multi-objective Problems,” IEEE Transactions on Evolutionary Computation, vol. 22, pp. 805-817, 09/19 2017.
- Zhou A., Qu B.-Y., Li H., Zhao S.-Z., Suganthan P. N., Zhang Q., “Multiobjective evolutionary algorithms: A survey of the state of the art,” Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 32-49, 2011/03/01/ 2011.
- Zitzler E., Deb K., Thiele L., “Comparison of Multiobjective Evolutionary Algorithms: Empirical Results,” Evolutionary computation, vol. 8, pp. 173-95, 02/01 2000.
Language: English
Page range: 139 - 170
Submitted on: Jun 10, 2025
Accepted on: Dec 23, 2025
Published on: Mar 17, 2026
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2026 Mohaddethe Nasrabadi, Mahdi khazaiepoor, Mahdi Kherad, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.