A tool for multiobjective evolutionary algorithms

Sağ, Tahir; Çunkaş, Mehmet

doi:10.1016/j.advengsoft.2009.01.001

Cited by 15 publications

(5 citation statements)

References 20 publications

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“…The performance measurements of well-known multi-objective evolutionary algorithms in MOEAT are done by benchmark problems [16].To evaluate the performance of the proposed algorithm, four test functions were adopted in [17]. Zitzler et al followed these guidelines and suggested six test problems [18], which were attractive for many researchers to evaluate the performance of their newly proposed approaches.…”

Section: Performance Evaluation Function Of Genetic Algorithmmentioning

confidence: 99%

Performance Test Functions of Genetic Algorithm

Yun

Yong

Chen

2013

AMM

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Due to the disadvantages of genetic algorithm such as the weaker ability for local search, premature convergence, random walk and problems related, and so on , the design and improvement of the algorithm is an important research direction of genetic algorithm. And evaluating the performance of algorithm systematically and scientifically is the key to test algorithm whether good or bad .The common method used to evaluate algorithm is test function, however, the existing literature on the optimization algorithm has different methods to evaluate the performance of algorithm, and there is no uniform test criteria. As for those questions above, This paper studies test functions of genetic algorithm, and analyses characteristics of the main test functions, which can be used as the basis of selection algorithm test functions.

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Section: Performance Evaluation Function Of Genetic Algorithmmentioning

confidence: 99%

Performance Test Functions of Genetic Algorithm

Yun

Yong

Chen

2013

AMM

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“…To date, various kinds of optimisation‐based tools have been developed. These offer an object‐oriented design for evaluating fitness functions using a variety of optimisation algorithms, which are written against the framework of Java (eg, Evolvica, JCLEC, and jMetal), C# (eg, MOEAT), or MATLAB (eg, YALMIP). However, among these tools, the identification of soil parameters has not received any attention.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of backtracking search algorithm for identifying soil parameters

Jin

Yin

2020

Num Anal Meth Geomechanics

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In this paper, an enhanced backtracking search algorithm (so-called MBSA-LS) for parameter identification is proposed with two modifications: (a) modifying the mutation of original backtracking search algorithm (BSA)considering the contribution of current best individual for accelerating convergence speed and (b) novelly incorporating an efficient differential evolution (DE) as local search for improving the quality of population. The proposed MBSA-LS is first validated with better performance than the original BSA and some other typical state-of-the-art optimization algorithms on a benchmark of soil parameter identification in terms of effectiveness, efficiency, and robustness. Then, the efficiency of the MBSA-LS is further illustrated by two representative cases: identifying soil parameters from both laboratory tests and field measurements. All comparisons demonstrate that the proposed MBSA-LS algorithm can give accurate results in a short time. Finally, to conveniently solve the problems of parameter identification, a practical tool ErosOpt for parameter identification is developed by integrating the proposed MBSA-LS and some other efficient algorithms for readers to conduct the parameter identification using optimisation algorithms. K E Y W O R D Sconstitutive model, graphical user interface, local search, optimisation, parameter identification, pressuremeter

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“…In some multi-objective studies, GA, fuzzy approach or taguchi method have been used to form multi-objective from single objective optimization [8][9][10]. As multi-objective evolutionary algorithms, vector evaluated genetic algorithm (VEGA) developed by Schaffer, multi-objective genetic algorithm (MOGA) suggested by Murata, niched pareto genetic algorithm (NPGA) recommended by Horn and Nafpliotis, non-dominated sorting genetic algorithm (NSGA) recommended by Srinivas and Deb, strength pareto evolutionary algorithm (SPEA) and the extension (SPEA2) proposed by Zitzler, pareto enveloped-base selection algorithm (PESA) proposed by Corne et al, and non-dominated sorting genetic algorithm II (NSGAII) were developed by Deb et al [11]. With these algorithms, an equivalent set of solutions is obtained to solve a problem.…”

Section: Introductionmentioning

confidence: 99%

“…With these algorithms, an equivalent set of solutions is obtained to solve a problem. When these algorithms are tested with test functions, it has been shown that NSGAII provides better convergence and wider solution distribution than other multi-objective evolutionary algorithms [11]. Obviously NSGAII, a second-generation effective algorithm after NSGA, provides a pareto-optimal approach to solutions [12].…”

Section: Introductionmentioning

confidence: 99%

An approach based on non-dominated sorting genetic algorithm III for design of permanent magnet synchronous motor

Mutluer¹

2020

ijisae

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Today due to industrial developments, the use of electric motors has increased in all fields. The increase also preceded the development of higher-specification motors. Although weight, cogging torque, torque ripples and drive technology etc. for the working area are important, the demand for the production of highly efficient and cost-effective motors has risen further due to the energy phenomenon in the world. High-quality algorithms are needed to achieve these objectives as well, because electric motor designs are multiparameter and nonlinear engineering problems. This study aims to provide a multi-purpose intelligent design with NSGAII and NSGAIII by selecting outputs such as efficiency and cost of permanent magnet synchronous motor as an objective function. The design was intended for low speed and high torque/volume applications and the motor geometry was thus chosen as surface-mounted and double-layer concentrated winding. The optimization results were tested with a finite element program. Both methods resulted in a 3% increase in efficiency and a 37% reduction in cost versus initial design. Also, according to the results obtained, although NSGA-II and NSGA-III achieved similar results, NSGA-III results showed a more robust and stable course than NSGA-II results. The compatibility of the design optimization and the results of numerical analysis are acceptable and highly satisfactory. So, it provides outputs to demonstrate the features of an electric motor design optimization.

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A tool for multiobjective evolutionary algorithms

Cited by 15 publications

References 20 publications

Performance Test Functions of Genetic Algorithm

Performance Test Functions of Genetic Algorithm

Enhancement of backtracking search algorithm for identifying soil parameters

An approach based on non-dominated sorting genetic algorithm III for design of permanent magnet synchronous motor

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