MOPSO algorithm and its application in multipurpose multireservoir operations

Abstract

The main reason for applying evolutionary algorithms in multi-objective optimization problems is to obtain near-optimal nondominated solutions/Pareto fronts, from which decision-makers can choose a suitable solution. The efficiency of multi-objective optimization algorithms depends on the quality and quantity of Pareto fronts produced by them. To compare different Pareto fronts resulting from different algorithms, criteria are considered and applied in multi-objective problems.Each criterion denotes a characteristic of the Pareto front. Thus, ranking approaches are commonly used to evaluate different algorithms based on different criteria. This paper presents three multi-objective optimization methods based on the multi-objective particle swarm optimization (MOPSO) algorithm. To evaluate these methods, bi-objective mathematical benchmark problems are considered. Results show that all proposed methods are successful in finding near-optimal Pareto fronts. A ranking method is used to compare the capability of the proposed methods and the best method for further study is suggested. Moreover, the nominated method is applied as an optimization tool in real multi-objective optimization problems in multireservoir system operations. A new technique in multi-objective optimization, called warm-up, based on the PSO algorithm is then applied to improve the quality of the Pareto front by single-objective search. Results show that the proposed technique is successful in finding an optimal Pareto front.Key words 9 9 9 9 multi-objective particle swarm optimization (MOPSO), multipurpose, multireservoir systems, optimization problems

f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management

“…Other characteristics of the algorithm in this method are similar to the SOPSO (Fallah-Mehdipour et al, 2011).…”

“…In a wide range of papers on multi-objective optimization, only the first three criteria (NS, S and GD) are addressed (Cabrera and Coello, 2010;Fallah-Mehdipour et al, 2011). But in this paper, the last two criteria (minimum Z 1 and Z 2 ) were also considered in the TOPSIS analysis to facilitate a robust performance evaluation.…”

Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems

“…Ostadrahimi et al (2011) improved the performance of the standard particle swarm optimization algorithm and incorporated a new strategic mechanism called multiswarm algorithm and used for multi-objective reservoir operation rules. Fallah-Mehdipour et al (2011) presented three multiobjective optimization methods based on multi-objective parti-cle swarm optimization (MOPSO) algorithm. To evaluate these methods, they considered bi-objective mathematical benchmark problems.…”

An iterative multi-objective particle swarm optimization-based control vector parameterization for state constrained chemical and biochemical engineering problems

“…This strategy helped MOPSO explore the search space more efficiently. Based on this, MOPSO has been applied to many optimization problems, such as multipurpose multireservoir operations [32], accident severity analysis [33] and so on.…”