Solution of Economic Load Dispatch Problem by Modified Whales Optimization Algorithm

Malik, Muhammad Usama; Zaid, Malik Muhammad; Ahmad, Aftab

doi:10.1109/raee.2019.8887044

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Although many WOA variants have been proposed for better optimization performance, they have not achieved optimal balance in their operations leading towards slow convergence speed and premature convergence. These drawbacks are obvious when solving power system optimization problems involving non-convex and non-linear fitness functions, particularly when considering modern day power system with complex inequality and equality constraints (Malik et al, 2019;Mohamed et al, 2018;Kumar et al, 2018). Hence, power optimization problem such as Reactive power optimization (RPO) is usually a good choice of application for assessing the practical performance of a newly proposed optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization Using New Enhanced Whale Optimization Algorithm

Rahman

Mohamad–Saleh

Sulaiman

2022

International Journal of Applied Metaheuristic Computing

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The standard Whale Optimization Algorithm (WOA) involves exploitation and exploration operations which require to be balanced for improved performance. This paper suggests a new enhanced WOA to improve the convergence speed and enhance the global optimum by balancing exploitation and exploration processes. New stages have been suggested at the hunting stages of the WOA to increase the exploitation capability. The performance of the modified algorithm has been analyzed on few commonly used test functions and Reactive Power Optimization (RPO) problem. The numerical studies have shown that the proposed WOA variant has outperformed other compared optimization algorithms in terms of global optimum and convergence speed.

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Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization Using New Enhanced Whale Optimization Algorithm

Rahman

Mohamad–Saleh

Sulaiman

2022

International Journal of Applied Metaheuristic Computing

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“…The field of nature-inspired EAs has continuous growth. Recently, many algorithms are proposed and applied to solve the EPD problem, such as Squirrel Search Algorithm [6], Cuckoo Search [11], Crow Search Algorithm [12], Whale Optimization Method [13], Antlion Optimization Algorithm [14], Moth Search Algorithm [15], etc. Firefly Algorithm (FFA) is one of the EAs which initially proposed to solve single-objective continuous optimization problems [16].…”

Section: Introductionmentioning

confidence: 99%

Pareto-based Multi-objective Firefly Algorithm with Hierarchical Clustering for Environmental-Economic Power Dispatch Problem

Spea

2021

ERJ. Engineering Research Journal

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This paper proposes a multi-objective firefly algorithm (MOFFA) to solve the environmental-economic power dispatch (EEPD) problem. Also, it presents a modified firefly algorithm (FFA) to solve the economic power dispatch (EPD) and the emission dispatch (ED) problems as single goals. The modifications proposed on the traditional FFA aim to improve its exploration and ensure the feasibility of the obtained solutions. The proposed MOFFA uses an external Pareto set to keep the non-dominated solutions, where a hierarchical clustering algorithm is used to get a representative and controlled set of Pareto-optimal solutions. The best compromise solution is also extracted from the Pareto set using an approach based on the fuzzy set theory. The constraints of EEPD, EPD, and ED problems are power balance constraint, generation limits constraint, and transmission power losses. To verify the effectiveness of the proposed algorithms, two methodologies are adopted and tested on the IEEE 30-bus test and the 10-unit test system with valve-point loading. In methodology 1, the EPD problem and the ED problem are solved separately using the modified FFA. In methodology 2, the EEPD is solved as a true multi-objective optimization problem using the proposed MOFFA. The simulation results and the statistical analysis ensure the high-quality solutions of the proposed algorithms and prove the ability of the MOFFA to produce well-distributed Pareto-optimal solutions.

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