A guided population archive whale optimization algorithm for solving multiobjective optimization problems

Got, Adel; Moussaoui, Abdelouahab; Zouache, Djaafar

doi:10.1016/j.eswa.2019.112972

Cited by 104 publications

(45 citation statements)

References 25 publications

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“…In time fitness and photometric fitness, the image objectives tend to create missions with closer waypoints and a greater number of flight layers, increasing the total mission time. There is no single ideal solution for such a situation instead of a set of optimal compensation solutions known as Pareto optimal solutions, called Pareto-optimal Front [ 40 ]. Several methods are present in the literature to explore the solutions present in the Pareto set.…”

Section: Proposed Frameworkmentioning

confidence: 99%

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

Biundini

Pinto

Melo

et al. 2021

Sensors

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Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain’s topographic features. In this sense, Coverage Path Planning (CPP) aims at finding the best path to coverage of a determined area respecting the operation’s restrictions. Photometric information from the terrain is used to create routes or even refine paths already created. Therefore, this research’s main contribution is developing a methodology that uses a metaheuristic algorithm based on point cloud data to inspect slope and dams structures. The technique was applied in a simulated and real scenario to verify its effectiveness. The results showed an increasing 3D reconstructions’ quality observing optimizing photometric and mission time criteria.

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Section: Proposed Frameworkmentioning

confidence: 99%

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

Biundini

Pinto

Melo

et al. 2021

Sensors

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“…Although the WOA is an efficient optimization algorithm in dealing with global optimization and engineering problems [30]- [33], but yet, it has the main shortcoming that is premature convergence, which leads the algorithm to get trapped into local optima easily. Many studies have been addressed in the literature to overcome this deficiency [34]- [44]. In this paper, enhanced whale optimization algorithms, namely EWOA and FC-EWOA, are proposed.…”

Section: Proposed Fc-ewoamentioning

confidence: 99%

“…Consequently, various modifications have been reported in the literature to enhance its exploration and exploitation capabilities and achieve better performance. Authors in [34] developed the WOA based on Pareto dominance to solve multiobjective optimization problems. This algorithm used an exterior archive as a storage for the non-dominated solutions detected during the optimization mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, modified versions of WOA have achieved remarkable results in other applications, such as water resources demand estimation [38], maximizing the power capture of variable-speed wind turbines [39], task allocation [40], parameter identification of solar cell diode model [41], quadratic assignment problem [42], terminal voltage control of fuel cells [43], and shortterm natural gas consumption prediction [44]. Although these modifications have yielded some performance enhancements to the conventional WOA, they still suffer from other drawbacks such as lack of exploitation accuracy [36]- [39], [40], lack of exploration accuracy and getting stuck in local optima [34], [39], [41], [42], [44], and low convergence rate [34], [35], [38]- [41], that need to be adequately addressed.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Fractional Chaotic Whale Optimization Algorithm for Parameter Identification of Isolated Wind-Diesel Power Systems

2020

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Parameters identification of isolated wind-diesel power systems (WDPS) is a significant issue in stability analysis of the power system as well as guaranteeing the power generation through the control system. In this paper, enhanced whale optimization algorithms (EWOA) are proposed to deal with the parameter identification problem of a WDPS system. The proposed EWOA effectively tackles the premature convergence problem of WOA by splitting the population into two subpopulations and updating the position of each whale according to the position of the best agent in its current subpopulation, the position of the other subpopulation's best agent, and the position of the best neighboring agent. Furthermore, fractional chaotic maps are embedded in the search process of EWOA to increase its performance in terms of accuracy. For validation purposes, the proposed algorithms are applied to identify the unknown parameters of WDPS, where different statistical analyzes and comparisons are carried out with other recent state-of-the-art algorithms. Simulation results confirm that the algorithms have less deviation in parameter estimation, more convergence speed, and higher precision in comparison with other algorithms. INDEX TERMS Optimization, parameter identification, whale optimization algorithm, wind-diesel power system.

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“…Got et al, [48] proposed a multi-objective algorithm by incorporating Pareto dominance and an external archive into a recently published algorithm, named Whale Optimization Algorithm [49], to deal with multiple objectives. Liu et al, [50] embedded the quantum approach into the PSO algorithm to the extended new multi-objective of this algorithm, named Multi-Objective Quantum-behaved Particle swarm optimization, to obtain promising results, in which cultural evolution mechanism was used to obtain high-quality results in Pareto optimal solutions.…”

Section: Mo-based Metaheuristic Algorithmsmentioning

confidence: 99%

Multi-Objective Volleyball Premier League algorithm

Moghdani

Salimifard

Demir

et al. 2020

Knowledge-Based Systems

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This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multiobjective algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems.

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A guided population archive whale optimization algorithm for solving multiobjective optimization problems

Cited by 104 publications

References 25 publications

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

Enhanced Fractional Chaotic Whale Optimization Algorithm for Parameter Identification of Isolated Wind-Diesel Power Systems

Multi-Objective Volleyball Premier League algorithm

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