Water cycle algorithm for solving constrained multi-objective optimization problems

Sadollah, Ali; Eskandar, Hadi; Kim, Joong Hoon

doi:10.1016/j.asoc.2014.10.042

Cited by 235 publications

(98 citation statements)

References 34 publications

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“…Therefore, the motivation is created to use the MOWCA (Sadollah et al, 2014(Sadollah et al, , 2015 for solving multi-objective portfolio optimisation problem. Moreover, the portfolio optimisation problem with multi-objective functions cannot be efficiently solved using traditionally approaches (Zhu et al, 2011).…”

Section: Water Cycle Algorithmmentioning

confidence: 99%

“…It means that this solution is a solution dominated by solutions A and B. In contrast, both solutions A and B can be considered as non-dominated solutions, as neither of them dominates each other (Sadollah et al, 2015).…”

Section: Standard Format Of Multi-objective Optimisation Problemsmentioning

confidence: 99%

“…distance (GD) metric, metric of spacing, diversity metric, and metric of maximum spread (MS) widely used in the literature (Sadollah et al, 2015). Veldhuizen and Lamont (1998) introduced the GD metric.…”

Section: Performance Metrics For Mopsmentioning

confidence: 99%

“…The evaporation process operator also is introduced to avoid premature (immature) convergence to local optima (exploitation phase) (Sadollah et al, 2015). Basically, evaporation causes sea water to evaporate as rivers/streams flow into the sea.…”

Section: Water Cycle Algorithmmentioning

confidence: 99%

“…Recently, the multi-objective water cycle algorithm (MOWCA) has been validated and implemented for solving constrained and unconstrained multi-criteria benchmark problems (Sadollah, Eskandar, & Kim, 2015;Sadollah, Eskandar, Kim, & Bahreininejad, 2014). The main objective of this article is to show the efficiency of WCA for solving portfolio optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The application of water cycle algorithm to portfolio selection

Moradi

Sadollah

Eskandar

et al. 2017

Economic Research-Ekonomska Istraživanja

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Portfolio selection is one of the most vital financial problems in literature. The studied problem is a nonlinear multi-objective problem which has been solved by a variety of heuristic and metaheuristic techniques. In this article, a metaheuristic optimiser, the multiobjective water cycle algorithm (MOWCA), is represented to find efficient frontiers associated with the standard mean-variance (M-V) portfolio optimisation model. The inspired concept of WCA is based on the simulation of water cycle process in the nature. Computational results are obtained for analyses of daily data for the period January 2012 to December 2014, including S&P100 in the US, Hang Seng in Hong Kong, FTSE100 in the UK, and DAX100 in Germany. The performance of the MOWCA for solving portfolio optimisation problems has been evaluated in comparison with other multi-objective optimisers including the NSGA-II and multiobjective particle swarm optimisation (MOPSO). Four well-known performance metrics are used to compare the reported optimisers. Statistical optimisation results indicate that the applied MOWCA is an efficient and practical optimiser compared with the other methods for handling portfolio optimisation problems.

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Section: Water Cycle Algorithmmentioning

confidence: 99%

Section: Standard Format Of Multi-objective Optimisation Problemsmentioning

confidence: 99%

Section: Performance Metrics For Mopsmentioning

confidence: 99%

Section: Water Cycle Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The application of water cycle algorithm to portfolio selection

Moradi

Sadollah

Eskandar

et al. 2017

Economic Research-Ekonomska Istraživanja

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Short-term hydrothermal coordination using water cycle algorithm with evaporation rate

Haroon

Malik

2017

Int. Trans. Electr. Energ. Syst.

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Summary The demand of electrical energy is increasing and so is its cost. The conventional means of generation of electrical are the hydel and thermal. Their optimized mutual operation results in the most economic electrical energy for the end user. Therefore, the optimized operation of hydel and thermal plants known as hydrothermal coordination (HTC) is one of the important and challenging tasks in the field of power system operation. This paper investigates a modified version of a new nature inspired algorithm, water cycle algorithm called as evaporation rate–based water cycle algorithm (ERWCA) for the solution of HTC. This algorithm has its roots from the natural water cycle. The raindrops form streams, which flow to the rivers and eventually into the sea. The streams either merge into rivers and sea or they are too slow to reach the rivers or sea; the evaporation process occurs, which again causes the rainfall. Hence, new streams will be created. In this paper, ERWCA has been successfully applied to the problem of HTC. The complex constraints of the hydel and thermal network especially the prohibited discharge zones of hydel plants and the ramp rates of thermal plants, which are rarely taken into account, have been incorporated. The comparison of the results with other nature inspired techniques already available in the literature like particle swarm optimization and differential evolution shows the strength and potential of this new technique ERWCA in obtaining the better quality result.

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An adaptive hydrologic cycle optimization algorithm for numerical optimization and data clustering

Yan

Niu

Chai

et al. 2022

Int J of Intelligent Sys

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The circulation and convergence of water in the hydrologic cycle process inspired us to design a new optimization algorithm, the Hydrologic Cycle Optimization (HCO) algorithm. In this study, a comprehensive demonstration of the HCO was presented. First, a simplified model of the hydrological cycle phenomenon was established. Then, the framework of HCO and its operators were discussed and verified in detail. Several experiments were done to test the optimization ability of the HCO algorithm. In the first experiment, the parameter settings were tested, and an adaptive version of the algorithm was proposed. Then the HCO was tested on 20 numeric optimization benchmark functions and eight data clustering sets, respectively, and compared with other algorithms. The experimental results showed that the HCO is superior to the compared algorithms, indicating that it is a competitive approach for numerical and engineering optimization problems.

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Water cycle algorithm for solving constrained multi-objective optimization problems

Cited by 235 publications

References 34 publications

The application of water cycle algorithm to portfolio selection

The application of water cycle algorithm to portfolio selection

Short-term hydrothermal coordination using water cycle algorithm with evaporation rate

An adaptive hydrologic cycle optimization algorithm for numerical optimization and data clustering

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