Multi-Objective Particle Swarm Optimization (MOPSO) method is known as a heuristic optimization technique thatThe multi-Objective does not guarantee to reach global optimality. Why the algorithm is convenient for most of the targeted applications? Are they other potential solution approaches? Is it conceivable to use standard optimization solvers like, e.g., CPLEX. The multi-Objective
Nawaraj Kumar Mahato I haven't used CPLEX yet, so can't tell too much about that.
If you are interested in nature/ evolutionary-based Multi-objective optimization algorithms, besides MOPSO, you may refer to any of the followings:
1. Non-dominated Sorting Genetic Algorithm (NSGA-II) - Deb, Kalyanmoy, Amrit Pratap, Sameer Agarwal, and T. A. M. T. Meyarivan. "A fast and elitist multiobjective genetic algorithm: NSGA-II." IEEE transactions on evolutionary computation 6, no. 2 (2002): 182-197.
2. NSGA-III- An updated version of NSGA-II
3. Non-dominated Sorting Whale Optimization Algorithm (NSWOA)- Jangir, Pradeep, and Narottam Jangir. "Non-dominated sorting whale optimization algorithm (NSWOA): a multi-objective optimization algorithm for solving engineering design problems." Global Journal of Research In Engineering (2017).
4. Non-dominated Sorting Differential Evolution (NSDE) - Angira, Rakesh, and B. V. Babu. "Non-dominated sorting differential evolution (NSDE): An extension of differential evolution for multi-objective optimization." In IICAI, pp. 1428-1443. 2005.
5. MOGBO- Article MOGBO: A New Multiobjective Gradient-Based Optimizer for Rea...
6. Multi-objective Firefly optimization- Yang, Xin-She. "Multiobjective firefly algorithm for continuous optimization." Engineering with computers 29, no. 2 (2013): 175-184.
It is to be noted the provided list is in no way a complete one and you may find many more of these in the literature.
Related to your query about 'inability of finding the global optimum by the MOPSO', I would link to say that in multi-objective optimization problem we usually look for a pareto-optimal solution instead of the global/ local optima as we have to compromise our solution through some sort of trade-off among a set of conflicting objectives.
Regarding the popularity of MOPSO, it can be said that like its single objective version (PSO), it provides competitive results in most cases.
In my opinion, the multi-objective metaheuristic is better than others.
Dear Nawaraj Kumar Mahato,
We can indeed point out that there is a difference between the two techniques in computing time, that of MOPSO is for certain applications is more demanding but of a better performance.
For more details and information about this subject, i suggest you to see links and attached files on topic.
https://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001990
Article Optimization of Continuous Berth Scheduling by Taking into A...
Article A Comprehensive Survey: Multi-Objective Particle Swarm Optim...
Article Optimizing a multi-product closed-loop supply chain using NS...
Best regards
if your problem is convex, you should choose convex optimization algorithms. CPLEX can be used for that
when your problem is non convex you can use metaheuristic optimization.
It depends on the dimension size and multi-modality level of the optimisation problem. If the problem size is small and convex, CPLEX can be a better option.
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