Chaotic particle swarm optimization algorithm for flexible process planning

Petrović, Milica; Mitić, Marko; Vuković, Najdan; Miljković, Zoran

doi:10.1007/s00170-015-7991-4

Cited by 52 publications

(21 citation statements)

References 31 publications

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“…However, by coupling PSO algorithm suitable for discrete optimisation and chaos theory through which various chaotic maps are implemented for enlarging the search space and enhancing its diversity, Petrović et al (2016) proposed a mechanism to prevent the premature convergence of PSO.…”

Section: Metaheuristicsmentioning

confidence: 99%

Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions

Jamalnia

Yang

Feili

et al. 2019

Int J Adv Manuf Technol

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This is the first literature survey of its kind on aggregate production planning (APP) under uncertainty. Different types of uncertainty, such as stochasticity, fuzziness and possibilistic forms, have been incorporated into many management science techniques to study APP decision problem under uncertainty. In current research, a wide range of the literature which employ management science methodologies to deal with APP in presence of uncertainty is surveyed by classifying them into five main categories: stochastic mathematical programming, fuzzy mathematical programming, simulation, metaheuristics and evidential reasoning. First, the preliminary analysis of the literature is presented by classifying the literature according to the abovementioned methodologies, discussing about advantages and disadvantages of these methodologies when applied to APP under uncertainty, and concisely reviewing the more recent literature. Then, APP literature under uncertainty is analysed from management science and operations management perspectives. Possible future research paths are also discussed on the basis of identified research trends and research gaps.

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Section: Metaheuristicsmentioning

confidence: 99%

Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions

Jamalnia

Yang

Feili

et al. 2019

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Indeed, the ergodicity property of chaos allows the improvement of optimisation algorithms by alleviating the lack of diversity and premature convergence problems. Already, chaotic sequences have been embedded in different meta-heuristic optimisation algorithms such as PSO [45], SA [46], artificial immune system algorithm [47], imperialist competitive algorithm [48], teaching-learning-based optimisation algorithm [49,50], bat algorithm [51], firefly algorithm [52], gravitational search algorithm [53], and Jaya algorithm [54]. The experimental validation of the aforementioned algorithms has revealed the potential of chaos as a persuasive and potent method for hybridisation.…”

Section: Related Workmentioning

confidence: 99%

Chaotic sine–cosine algorithm for chance‐constrained economic emission dispatch problem including wind energy

Guesmi

Farah

Ismail

et al. 2020

IET Renewable Power Generation

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One of the most effective approaches to reduce carbon emissions is the integration of renewable energy sources into electrical power networks. Currently, wind turbines are the fastest growing among all renewable sources. With the integration of wind farms into electrical grids, the economic emission dispatch (EED) problem is becoming more complicated due to the stochastic availability of wind energy. In this study, a new approach is proposed to solve the EED problem incorporating wind farms. The problem is formulated as a chance-constrained problem to deal with the stochastic characteristic of wind power. A novel chaotic sine-cosine algorithm (CSCA) is proposed to provide the optimal generation schedule to minimise simultaneously the generation cost and emission. Some weakness has been encountered in exploitation and exploration capabilities in standard sine cosine algorithm (SCA). Hence, the chaos is integrated into the original SCA to improve its performance. In addition, a new mutation strategy is added to the SCA. In this study, the new algorithm is based on three mutually exclusive equations. The new technique is applied on the 69-bus ten-unit and 40-unit test systems with and without wind energy. The results performed by CSCA are compared with those generated by other recent techniques.

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“…Moreover, many efforts were also devoted to GAbased hybrid methods for optimisation of machining process. Petrovic et al (2016) utilised PSO algorithm and chaos theory to optimise process plans, in which PSO was used in early stages of the optimisation process by implementing ten different chaotic maps that enlarged the search space and provided diversity. In addition, Rowe et al (1996) reported an application of artificial intelligence in CNC grinding, including knowledge based and expert systems, fuzzy logic systems, and neural network systems.…”

Section: Related Workmentioning

confidence: 99%

A big data analytics based machining optimisation approach

Yin

Wang

2018

J Intell Manuf

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Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm.

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Chaotic particle swarm optimization algorithm for flexible process planning

Cited by 52 publications

References 31 publications

Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions

Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions

Chaotic sine–cosine algorithm for chance‐constrained economic emission dispatch problem including wind energy

A big data analytics based machining optimisation approach

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