Multi-objective Grey Wolf Optimizer

Mirjalili, Seyedali; Dong, Jin Song

doi:10.1007/978-3-030-24835-2_5

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The multi-objective greywolf optimization was performed on the objective functions. One hundred iterations were adopted and the MATLAB codes, implemented on an 8 GB RAM Intel(R) Core(TM) i3-5005U CPU @ 2.00GHz laptop, were edited and adapted from Mirjalili [46]. The set values of the hyperparameters are given in Table 8.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Multi-Objective Optimization of a Solar-Assisted Combined Cooling, Heating and Power Generation System Using the Greywolf Optimizer

Ukaegbu,

Tartibu,

Lim

2023

Algorithms

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Energy demand and consumption have, in recent times, witnessed a rapid proliferation influenced by technological developments, increased population and economic growth. This has fuelled research trends in the domain of energy management employing tri-generation systems such as combined cooling, heating and power (CCHP) systems. Furthermore, the incorporation of renewable energy, especially solar energy, to complement the thermal input of fossil fuels has facilitated the effectiveness and sustainability of CCHP systems. This study proposes a new approach to improve the overall efficiency of CCHP systems and to compute optimal design parameters in order to assist decision makers to identify the best geometrical configuration. A multi-objective optimization formulation of a solar-assisted CCHP system was adopted to maximize the net power and exergy efficiency and to minimize the CO2 emission using the greywolf optimization technique. In addition, the effects of the decision variables on the objective functions were analysed. The proposed optimization approach yielded 100 set of Pareto optimal solutions which would serve as options for the decision maker when making a selection to choose from when seeking to improve the performance of a solar-assisted CCHP system. It also yielded higher exergy efficiency and lower CO2 emission values when compared with a similar study. The results obtained indicate that a system with high net power output does not necessarily translate to a highly efficient system. Additionally, minimal CO2 emissions were recorded for a system with low compression ratio, low combustion chamber inlet temperature and high inlet turbine temperature. This study demonstrates that the proposed approach is potentially suitable for the optimization of a solar-assisted CCHP system.

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Section: Multi-objective Optimizationmentioning

confidence: 99%

Multi-Objective Optimization of a Solar-Assisted Combined Cooling, Heating and Power Generation System Using the Greywolf Optimizer

Ukaegbu,

Tartibu,

Lim

2023

Algorithms

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“…Grey Wolf optimizer (GWO) algorithm, developed by Mirjalili in 2014, is a recent smart swarm-based meta-heuristic approach [50][51][52]. This algorithm mimics the leadership hierarchy and hunting process of Grey wolves in the wildlife.…”

Section: Grey Wolf Optimizer (Optimization Of Damping Factor's Value)mentioning

confidence: 99%

“…The process of hunting prey by a group of wolves [51]. The mentioned above social hierarchy and hunting process of Grey wolves have been mathematically modeled in GWO, as follows [51,52]:…”

Section: Grey Wolf Optimizer (Optimization Of Damping Factor's Value)mentioning

confidence: 99%

Study of a New Hybrid Optimization-Based Method for Obtaining Parameter Values of Solar Cells

Tchoketch_Kebir¹

2021

Solar Cells - Theory, Materials and Recent Advances

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This chapter presents a comprehensive study of a new hybrid method developed for obtaining the electrical unknown parameters of solar cells. The combination of a traditional method and a recent smart swarm-based optimization method is done, with a big focus on the application of the topic of artificial intelligence algorithms into solar photovoltaic production. The combined approach was done between the traditional method, which is the noniterative Levenberg-Marquardt technic and between the recent meta-heuristic optimization technic, called Grey Wolf optimizer algorithm. For comparison purposes, some other classical solar cell parameter determination optimization-based methods are carried out, such as the numerical (iterative, noniterative) methods, the meta-heuristics (evolution, human, physic, and swarm) methods, and other hybrid methods. The final obtained results show that the used hybrid method outperforms the above-mentioned classical methods, under this study.

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“…The following is a typical classification of methods based on preferences, depending on how they are expressed by the DM [ 16 – 18 ]: (i) a priori methods, where preferences are expressed before calculating PO solutions, for example, through a utility function [ 19 ] or by an RP [ 20 ]; (ii) a posteriori methods, in which the DM chooses the solution of her/his preference after a set of efficient solutions has been calculated (for example, [ 21 , 22 ]); (iii) interactive methods, where the DM guides the search with a utility function, and this function may change during the optimization process because of the new information acquired (for example, [ 23 , 24 ]); and finally, (iv) methods not based on preferences, where additional information on preferences is not available, and the idea is to find a balance between the objectives [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Reference Point‐Based Evolutionary Algorithm Solves Multi and Many‐Objective Optimization Problems: Method and Validation

Jameel

Abouhawwash

2023

Computational Intelligence and Neuroscience

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The integration of a decision maker’s preferences in evolutionary multi-objective optimization (EMO) has been a common research scope over the last decade. In the published literature, several preference-based evolutionary approaches have been proposed. The reference point-based non-dominated sorting genetic (R-NSGA-II) algorithm represents one of the well-known preference-based evolutionary approaches. This method mainly aims to find a set of the Pareto-optimal solutions in the region of interest (ROI) rather than obtaining the entire Pareto-optimal set. This approach uses Euclidean distance as a metric to calculate the distance between each candidate solution and the reference point. However, this metric may not produce desired solutions because the final minimal Euclidean distance value is unknown. Thus, determining whether the true Pareto-optimal solution is achieved at the end of optimization run becomes difficult. In this study, R-NSGA-II method is modified using the recently proposed simplified Karush–Kuhn–Tucker proximity measure (S-KKTPM) metric instead of the Euclidean distance metric, where S-KKTPM-based distance measure can predict the convergence behavior of a point from the Pareto-optimal front without prior knowledge of the optimum solution. Experimental results show that the algorithm proposed herein is highly competitive compared with several state-of-the-art preference-based EMO methods. Extensive experiments were conducted with 2 to 10 objectives on various standard problems. Results show the effectiveness of our algorithm in obtaining the preferred solutions in the ROI and its ability to control the size of each preferred region separately at the same time.

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Multi-objective Grey Wolf Optimizer

Cited by 6 publications

References 25 publications

Multi-Objective Optimization of a Solar-Assisted Combined Cooling, Heating and Power Generation System Using the Greywolf Optimizer

Multi-Objective Optimization of a Solar-Assisted Combined Cooling, Heating and Power Generation System Using the Greywolf Optimizer

Study of a New Hybrid Optimization-Based Method for Obtaining Parameter Values of Solar Cells

A Reference Point‐Based Evolutionary Algorithm Solves Multi and Many‐Objective Optimization Problems: Method and Validation

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