A new Kriging–Bat Algorithm for solving computationally expensive black-box global optimization problems

Saad, Abdulbaset; Dong, Zuomin; Buckham, Bradley J.; Crawford, Curran; Younis, Adel; Karimi, Mohammad

doi:10.1080/0305215x.2018.1461853

Cited by 12 publications

(3 citation statements)

References 35 publications

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“…Creating three optimization objectives. The prediction parameterf ,ĝ i and RMSE related to the objective and constraints can be calculated by the kriging model (see Equations (8) and (9)). The three optimization objectives in Equation (16) will be formed by the prediction parameters.…”

Section: The Kmcgo Methodsmentioning

confidence: 99%

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A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Shen

Zi-liang

et al. 2021

Mathematics

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The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

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Section: The Kmcgo Methodsmentioning

confidence: 99%

“…The kriging-based EGO (efficient global optimization) method [7] and its extensions [8][9][10][11][12][13] have solved some optimization problems of expensive black-box objectives or constraints. Most of them are single-objective optimization approaches, which can only get one sampling point in per infill search loop.…”

Section: Introductionmentioning

confidence: 99%

A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Shen

Zi-liang

et al. 2021

Mathematics

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“…Four strategies were also proposed in (Bahmani-Firouzi et al 2014) for updating the bat velocities in which an accumulator for each strategy is computed and used to determine the probability of selecting that strategy. (Saad et al 2018) recently utilized a kriging surrogate model to solve Computationally Expensive Black-Box optimization problems. Despite the several efforts, the possibilities for enhancement are still on, as there is no algorithm that is ultimately perfect as it is indicated by the no free lunch theorem (Ho, Y.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive learning bat algorithm for optimal coordinated tuning of power system stabilizers and static VAR compensator in power systems

Baadji

Bentarzi

Bakdi

2019

Engineering Optimization

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This article presents a novel Comprehensive Learning Bat Algorithm (CLBAT) for the optimal coordinated design of Power System Stabilizers (PSSs) and Static Var Compensator (SVC) for damping electromechanical oscillations in multimachine power systems considering wide range of operating conditions. The CLBAT incorporates a new comprehensive learning strategy (CLS) to improve the micro-bats cooperation, location update is also improved to maintain the bats diversity and to prevent premature convergence through a novel adaptive search strategy based on the relative travelled distance. In addition, the proposed elitist learning strategy (ELS) speeds up the convergence during the optimization process and drives the global best solution (gbest) toward promising regions. The superiority of the CLBAT over recent algorithms is first demonstrated via several experiments and comparisons through benchmark functions. The developed algorithm ensures convergence speed, credibility, computational resources, and optimal tuning of PSSs and SVCs of multimachine systems under different operating conditions through eigenanalysis, nonlinear simulation, and performance indices.

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Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics

Osaba

Yang

Ser

2020

Nature-Inspired Computation and Swarm Intelligence

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A new Kriging–Bat Algorithm for solving computationally expensive black-box global optimization problems

Cited by 12 publications

References 35 publications

A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Comprehensive learning bat algorithm for optimal coordinated tuning of power system stabilizers and static VAR compensator in power systems

Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics

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