A chaotic non-dominated sorting genetic algorithm for the multi-objective automatic test task scheduling problem

Lu, Hui; Niu, Ruiyao; Liu, Jing; Zhu, Zhiwei

doi:10.1016/j.asoc.2012.10.001

Cited by 56 publications

(30 citation statements)

References 30 publications

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“…Similar to other scheduling problems, the TTSP is one kind of combination optimization problems. It is illustrated to be an NP-hard problem through the analysis of the nature of the problem carried out by many researchers [1][2][3]. In addition, through the fitness distance analysis [4], we know that the TTSP has many local optima.…”

Section: Introductionmentioning

confidence: 99%

“…Different from the hybrid method using different kinds of algorithms, using chaos in the evolutionary process represents its advantages in improving the searching ability. Lu et al proposed a chaotic nondominated sorting genetic algorithm for the multiobjective test task scheduling problem and validated the best performance in convergence and diversity through the experiment and analysis [1]. Donald et al utilized the chaos-induced discrete self-organizing migrating algorithm to solve the lot-streaming flow shop scheduling problem with setup time [7].…”

Section: Introductionmentioning

confidence: 99%

“…For the TTSP, notice that the previous studies mostly aim at a single objective and only few papers focus on the multiobjective problem [1,5]. Through the analysis of our previous related work for the multiobjective TTSP, we know the multiobjective evolutionary algorithm based on decomposition (MOEA/D) exhibited the best performance in solving the TTSP [5] in the aspect of convergence and diversity.…”

Section: Introductionmentioning

confidence: 99%

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Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem

Yin

Wang

et al. 2014

Mathematical Problems in Engineering

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Test task scheduling problem (TTSP) is a complex optimization problem and has many local optima. In this paper, a hybrid chaotic multiobjective evolutionary algorithm based on decomposition (CMOEA/D) is presented to avoid becoming trapped in local optima and to obtain high quality solutions. First, we propose an improving integrated encoding scheme (IES) to increase the efficiency. Then ten chaotic maps are applied into the multiobjective evolutionary algorithm based on decomposition (MOEA/D) in three phases, that is, initial population and crossover and mutation operators. To identify a good approach for hybrid MOEA/D and chaos and indicate the effectiveness of the improving IES several experiments are performed. The Pareto front and the statistical results demonstrate that different chaotic maps in different phases have different effects for solving the TTSP especially the circle map and ICMIC map. The similarity degree of distribution between chaotic maps and the problem is a very essential factor for the application of chaotic maps. In addition, the experiments of comparisons of CMOEA/D and variable neighborhood MOEA/D (VNM) indicate that our algorithm has the best performance in solving the TTSP.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem

Yin

Wang

et al. 2014

Mathematical Problems in Engineering

Self Cite

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“…Mahdiyeh [27] introduced chaotic sequence into particle swarm optimization to improve global searching capability and escape premature convergence to local minima for a power system stabilizer design. Although researchers have studied approaches that combined chaos with evolutionary algorithms in various fields, the research on combing chaotic operators with NSGA-II has not been well addressed [22,24,26] and will be explored in detail in this study. Meanwhile, it is also the first time that adopting the NSGA-II combined with chaotic operators in the domain of HEV as in this study.…”

Section: Introductionmentioning

confidence: 99%

“…NSGA-II was presented by Deb in 2002 and has been successfully applied to optimize reactive power dispatch problems [21], automatic test task scheduling problems [22], propulsion system of marine vessels [23], and other optimization problems. This method can effectively obtain improved spreading solutions.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Energy Management Optimization for a Parallel Hybrid Electric Vehicle using Chaotic Non-dominated sorting Genetic Algorithm

Liang

Zhang

et al. 2015

Automatika

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Original scientific paperThis paper presented a parallel hybrid electric vehicle (HEV) equipped with a hybrid energy storage system. To handle complex energy flow in the powertrain system of this HEV, a fuzzy-based energy management strategy was established. A chaotic multi-objective genetic algorithm, which optimizes the parameters of fuzzy membership functions, was also proposed to improve fuel economy and HC, CO, and NOx emissions. The main target of this algorithm is to escape from local optima and obtain high quality trade-off solutions. Chaotic initialization operator, chaotic crossover and mutation operators, chaotic disturbance operator, and chaotic local search operator were integrated into non-dominated sorting genetic algorithm II (NSGA-II) to form this new algorithm named chaotic NSGA-II (C-NSGA-II). Simulation results and comparisons demonstrated that chaotic operators can enhance searching ability for optimal solutions. In conclusion, C-NSGA-II is suitable for solving HEV energy management optimization problem.Key words: Chaotic operator, Fuzzy logic, Hybrid electric vehicle, Multi-objective optimization, NSGA-II Neizrazita strategija optimizacije potrošnje energije za paralelno hibridno električno vozilo korištenjem kaotičnog nedominirajućeg genetskog algoritma sortiranja. Ovaj rad prikazuje paralelno hibridno električno vozilo (HEV) opremljeno hibridnim spremnikom energije. Kako bi se omogućila funkcionalnost pogonskog sklopa ovakvog HEV-a korištena je strategija raspolaganja energijom zasnovana na neizrazitoj logici. Takoer, prikazan je više kriterijski genetski algoritam kaosa za optimiranje parametara neizrazite funkcije povezanih s ekonomskim pokazateljem te pokazateljima emisije HC-a, CO-a i NOx-a. Osnovni cilj algoritma je omogućiti izlazak iz lokalnih optimuma i uspostavljanjem kompromisa omogućiti dosezanje boljih rješenja. Kaotični inicijalizacijski operator, kaotično križanje i operator mutacije, kaotični operator poremećaja i kaotični operator lokalnog pretraživanje uključeni su u nedominirajući genetski algoritam sortiranja II (NSGA-II) u svrhu formulacije novog problema nazvanog kaotični NSGA-II (C-NSGA-II). Simulacijski rezultati i usporedbe prikazuju kako kaotični operator može povećati uspješnost traženja optimalnog rješenja. Zaključno, C-NSGA-II je primjeren za rješavanje problema raspolaganja energijom u HEV-u.Ključne riječi: operator kaosa, neizrazita logika, hibridna električna vozila, više kriterijska optimizacija, NSGA-II

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Brain Storm Optimization for Test Task Scheduling Problem

Zhou

Cheng

et al. 2019

Brain Storm Optimization Algorithms

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A chaotic non-dominated sorting genetic algorithm for the multi-objective automatic test task scheduling problem

Cited by 56 publications

References 30 publications

Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem

Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem

Fuzzy Energy Management Optimization for a Parallel Hybrid Electric Vehicle using Chaotic Non-dominated sorting Genetic Algorithm

Brain Storm Optimization for Test Task Scheduling Problem

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