The application of water cycle algorithm to portfolio selection

Moradi, Mohammad Ali; Sadollah, Ali; Eskandar, Hoda; Eskandar, Hadi

doi:10.1080/1331677x.2017.1355254

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…17is written for a stream which directly connected to the sea. This Equation helps to enhance the exploration near the sea in the feasible region [33,36].…”

Section: Condition For Evaporation and Rainingmentioning

confidence: 99%

Online implementation of time augmentation of over current relay coordination using water cycle algorithm

Kudkelwar

Sarkar

2019

SN Appl. Sci.

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In the present paper, a technique is presented for new investigations for the constrained type optimal coordination of over current relay problem using the water cycle algorithm (WCA). It is shown that the operating time of relay mainly depends upon the time dial setting (TDS) and plug settings. The paper introduces a novel metaheuristic optimizer algorithm, WCA, encouraged by the water sequence process and movements of rivers and flows on the way to the sea. Results are presented for the application of the methodology on a distribution system of single-end one loop system, Single-end multi-loop system and single-end multi-parallel feeder distribution system. Subsequently, the results find the minimal operating time of relay with optimized TDS for over current relay. The result reveals the effectiveness and superiority of the proposed WCA over the other techniques presented in the literature in terms of accuracy, input parameter and iterations.

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“…17is written for a stream which directly connected to the sea. This Equation helps to enhance the exploration near the sea in the feasible region [33,36].…”

Section: Condition For Evaporation and Rainingmentioning

confidence: 99%

Online implementation of time augmentation of over current relay coordination using water cycle algorithm

Kudkelwar

Sarkar

2019

SN Appl. Sci.

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“…The various nature-inspired algorithms can select an optimum portfolio, Moradi et al [16] presented a multiobjective water cycle strategy to resolve the mean-variance PSP, and the proposed algorithm was found more effective than other algorithms. Strumberger et al [17] presented a moth search approach for portfolio optimization.…”

Section: Introductionmentioning

confidence: 99%

Portfolio selection model using teaching learning-based optimization approach

Kumar

Shahid

2023

IJ-AI

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<p>Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem. However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization problems. This article presents a portfolio selection model using the TLBO approach to maximize the portfolio's Sharpe Ratio. The Sharpe ratio combines both expected return and risk. This algorithm model the natural teaching process of the classroom with two main phases, viz., teaching and learning. Performance analysis has been undertaken to investigate the suitability of TLBO based solution approach by comparing it with genetic algorithm (GA) and particle swarm optimization (PSO) on the real datasets, Deutscher Aktienindex (DAX) 100, Hang Seng 31, Standard and Poor’s (S&P) 100, financial times stock exchange (FTSE) 100, and Nikkei 225. The empirical results verify the superiority of the TLBO over GA and PSO.</p>

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“…Due to the complexity of the problem, there is a crucial need for a powerful optimization technique to find the optimal solution which satisfies the objective and the constraints [29][30][31][32][33]. The water cycle optimization algorithm is applied to solve the economical emission dispatch unit commitment problem.…”

mentioning

confidence: 99%

Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization

ElAzab¹,

Swief²,

El-Amary³

et al. 2018

Energies

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This paper presents a trustworthy unit commitment study to schedule both Renewable Energy Resources (RERs) with conventional power plants to potentially decarbonize the electrical network. The study has employed a system with three IEEE thermal (coal-fired) power plants as dispatchable distributed generators, one wind plant, one solar plant as stochastic distributed generators, and Plug-in Electric Vehicles (PEVs) which can work either loads or generators based on their charging schedule. This paper investigates the unit commitment scheduling objective to minimize the Combined Economic Emission Dispatch (CEED). To reduce combined emission costs, integrating more renewable energy resources (RER) and PEVs, there is an essential need to decarbonize the existing system. Decarbonizing the system means reducing the percentage of CO 2 emissions. The uncertain behavior of wind and solar energies causes imbalance penalty costs. PEVs are proposed to overcome the intermittent nature of wind and solar energies. It is important to optimally integrate and schedule stochastic resources including the wind and solar energies, and PEVs charge and discharge processes with dispatched resources; the three IEEE thermal (coal-fired) power plants. The Water Cycle Optimization Algorithm (WCOA) is an efficient and intelligent meta-heuristic technique employed to solve the economically emission dispatch problem for both scheduling dispatchable and stochastic resources. The goal of this study is to obtain the solution for unit commitment to minimize the combined cost function including CO 2 emission costs applying the Water Cycle Optimization Algorithm (WCOA). To validate the WCOA technique, the results are compared with the results obtained from applying the Dynamic Programming (DP) algorithm, which is considered as a conventional numerical technique, and with the Genetic Algorithm (GA) as a meta-heuristic technique.

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The application of water cycle algorithm to portfolio selection

Cited by 9 publications

References 38 publications

Online implementation of time augmentation of over current relay coordination using water cycle algorithm

Online implementation of time augmentation of over current relay coordination using water cycle algorithm

Portfolio selection model using teaching learning-based optimization approach

Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization

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