A modified whale optimization algorithm for large-scale global optimization problems

Sun, Youxian; Wang, Xilu; Chen, Yahuan; Liu, Zujun

doi:10.1016/j.eswa.2018.08.027

Cited by 192 publications

(90 citation statements)

References 32 publications

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“…In this subsection, some advanced WOA variants were employed to verify the proposed MCSWOA. These advanced WOA variants included CWOA [34], IWOA [28], Lion_Whale [35], LWOA [36], MWOA [37], OBWOA [27], PSO_WOA [38], RWOA [39], SAWOA [40], WOA−CM [41], and WOABHC [42]. The experimental results are summarized in Table 11.…”

Section: Comparison With Advanced Woa Variantsmentioning

confidence: 99%

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their efficacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modified search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by differential evolution are designed to balance the exploration and exploitation; (ii) a crossover operator based on the above modified search strategies is introduced to meet the search-oriented requirements of different dimensions; and (iii) a selection operator instead of the “generate-and-go” operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to five PV types. Both single diode and double diode models are used to model these five PV types. The good performance of MCSWOA is verified by various algorithms.

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Section: Comparison With Advanced Woa Variantsmentioning

confidence: 99%

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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“…The Whale Optimization Algorithm (WOA) is a recently developed algorithm based on the hunting behavior of the humpback whale [8]. WOA has a strong global search capacity due to its distinctive optimization mechanism [9]. In addition, WOA is less parameter-dependent and has a straightforward implementation [9].…”

Section: Introductionmentioning

confidence: 99%

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

et al. 2020

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The potentially many software system input combinations make exhaustive testing practically impossible. To address this issue, combinatorial t-way testing (where t indicates the interaction strength, i.e. the number of interacting parameters (input)) was adopted to minimize the number of cases for testing. Complimentary to existing testing techniques (e.g. boundary value, equivalence partitioning, cause and effect graphing), combinatorial testing helps to detect faults caused by the faulty interaction between input parameters. In the last 15 years, applications of meta-heuristics as the backbone of t-way test suite generation have shown promising results (e.g. Particle Swarm Optimization, Cuckoo Search, Flower Pollination Algorithm, and Hyper-Heuristics (HHH), to name a few). Supporting the new free lunch theorem, as well as potentially offering new insights into the whole process of t-way generation, this paper proposes a new strategy with constraint support based on the Whale Optimization Algorithm (WOA). Our work is the first attempt to adopt the WOA as part of a search-based software engineering (SBSE) initiative for t-way test suite generation with constraint support. The experimental results of the test-suite generation indicate that WOA produces competitive outcomes compared to some selected single-based and population-based metaheuristic algorithms.

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“…Whale optimization (WO) is a recently developed optimization algorithm that mimics the social behavior of humpback whales. [24][25][26] WO is based on bubble-net hunting behavior of humpback whales. WO composes of three steps of hunting, which are encircling, attacking, and searching the prey.…”

Section: Introductionmentioning

confidence: 99%

Selective harmonic elimination in hybrid cascaded multilevel inverter using modified whale optimization

Routray

Singh

Mahanty

2019

Int Trans Electr Energ Syst

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Summary This article presents a modified whale optimization (MWO) for harmonic mitigation in a three‐phase 11‐level hybrid cascaded multilevel inverter (HC‐MLI). The proposed MWO has improved rate of convergence as compared with conventional whale optimization (WO). This is achieved by improving the exploration‐exploitation feature of the conventional WO. Selective harmonic elimination–pulse width modulation technique is applied through MWO for generating the optimal switching angles of the HC‐MLI so as to eliminate the lower‐order harmonics such as 5th, 7th, 11th, and 13th from the output voltage. In the proposed MWO optimized HC‐MLI, capacitor voltage balance is made possible even at higher modulation indices using the available redundant switching states of HC‐MLI. The proposed work is useful where the HC‐MLI has to work at the low switching frequency and the conversion efficiency is of prime importance such as medium‐ and high‐power motor drives, static synchronous compensators, and active power filters. The performance of the proposed work is validated through simulation and experimentation on a 1.5‐kW prototype. The results obtained show that the MWO algorithm is more efficient and accurate than other reported algorithms such as genetic algorithm, particle swarm optimization, and WO in terms of performance, harmonic mitigation, and rate of convergence.

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A modified whale optimization algorithm for large-scale global optimization problems

Cited by 192 publications

References 32 publications

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

Selective harmonic elimination in hybrid cascaded multilevel inverter using modified whale optimization

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