Chaos-enhanced synchronized bat optimizer

Yu, Honggang; Zhao, Nannan; Wang, Pengjun; Chen, Huiling; Li, Chengye

doi:10.1016/j.apm.2019.09.029

Cited by 92 publications

(31 citation statements)

References 46 publications

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“…Since the 1980s, scholars have studied the job scheduling problem of parallel batch machines extensively [1]. In this section, we review the results of research dealing with different job sizes and minimization of the maximum completion time [14][15][16][17][18][19][20].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

Zhang

Song

et al. 2020

Applied Sciences

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With the rapid economic development, manufacturing enterprises are increasingly using an efficient workshop production scheduling system in an attempt to enhance their competitive position. The classical workshop production scheduling problem is far from the actual production situation, so it is difficult to apply it to production practice. In recent years, the research on machine scheduling has become a hot topic in the fields of manufacturing systems. This paper considers the batch processing machine (BPM) scheduling problem for scheduling independent jobs with arbitrary sizes. A novel fast parallel batch scheduling algorithm is put forward to minimize the makespan in this paper. Each of the machines with different capacities can only handle jobs with sizes less than the capacity of the machine. Multiple jobs can be processed as a batch simultaneously on one machine only if their total size does not exceed the machine capacity. The processing time of a batch is determined by the longest of all the jobs processed in the batch. A novel and fast 4.5-approximation algorithm is developed for the above scheduling problem. For the special case of all the jobs having the same processing times, a simple and fast 2-approximation algorithm is achieved. The experimental results show that fast algorithms further improve the competitive ratio. Compared to the optimal solutions generated by CPLEX, fast algorithms are capable of generating a feasible solution within a very short time. Fast algorithms have less computational costs.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

Zhang

Song

et al. 2020

Applied Sciences

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“…The current work of this paper aims to develop an intelligent decision model based on the KNN classifier. Compared with the traditional gradient descent algorithm [1], meta-heuristic algorithms, as a general solution of optimization problem, has the characteristics of fast convergence speed and strong global search ability [2]- [20]. The meta-heuristic algorithms such as bacterial colony optimization (BCO) [21], genetic algorithm (GA) [22], fruit fly optimization algorithm (FOA) [23], and particle swarm optimization (PSO) [24] have shown good performance in tackling with many tasks in the area of feature selection.…”

Section: Introductionmentioning

confidence: 99%

Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers

Tang

Lin

et al. 2020

IEEE Access

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Understanding the green consumption behaviors of college students is highly demanded to update the public and educational policies of universities. For this purpose, this research is devoted to advance an efficient model for identifying prominent features and predicting the green consumption behaviors of college students. The proposed prediction model is based on the K-Nearest Neighbor (KNN) with an effective swarm intelligence method, which is called OBLFA_GWO. The optimization core takes advantage of the firefly algorithm (FA) and opposition-based learning (OBL) to mitigate the immature convergence of the grey wolf algorithm (GWO). In the proposed prediction framework, OBLFA_GWO is utilized to identify influential features. Then, the enhanced KNN model is used to identify the importance and interrelationships of features in samples and construct an effective and stable predictive model for decision support. Five other wellknown algorithms are employed to validate the effectiveness of the proposed OBLFA_GWO strategy using 13 benchmark test problems. Also, the non-parametric statistical Wilcoxon sign rank and Friedman tests are conducted to validate the significance of the proposed OBLFA_GWO against other peers. Experimental results indicate that the FA and OBL can significantly boost the core exploratory and exploitative trends of GWO in dealing with the optimization tasks. Also, the OBLFA_GWO-based KNN (OBLFA_GWO-KNN) model is compared with four classical classifiers, such as kernel extreme learning machine (KELM), backpropagation neural network method (BPNN), and random forest (RF) and five advanced feature selection methods in terms of four standard evaluation indexes. The experimental results show that the classification accuracy of the proposed OBLFA_GWO-KNN can reach to 96.334 % on the real-life dataset collected from nine universities. Also, the proposed binary OBLFA_GWO algorithm has improved the classification performance of KNN compared to the other peers. Hopefully, the established adaptive OBLFA_GWO-KNN model can be considered as a useful tool for predicting students' behavior of green consumption. INDEX TERMS K-nearest neighbor, firefly algorithm, grey wolf algorithm, opposition-based learning, green consumption behavior, feature selection. The associate editor coordinating the review of this manuscript and approving it for publication was Josue Antonio Nescolarde Selva .

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“…These intelligent algorithms often show better results than traditional gradient-based algorithms [17]. Some of these intelligent algorithms, like the whale optimization algorithm (WOA) [14], [18]- [21], bat algorithm (BA) [22], [23], differential evolution (DE) [24], fruit fly optimization algorithm (FOA) [25]- [29], moth-flame optimization algorithm (MFO) [30]- [33], ant colony optimization algorithm (ACO) [34], [35], grey wolf optimizer (GWO) [36]- [38], grasshopper optimization algorithm (GOA) [39], fireworks algorithm (FWA) [40], particle swarm optimization (PSO) [41]- [43], salp swarm algorithm (SSA) [44], Harris hawks optimization (HHO) [45]- [47], and bacterial foraging optimization (BFO) [15], [48]- [50], have tackled various optimization cases. In 2016, Mirjalili [51] proposed a new metaheuristic algorithm, namely the sine cosine algorithm (SCA).…”

Section: Introductionmentioning

confidence: 99%

Rationalized Sine Cosine Optimization With Efficient Searching Patterns

et al. 2020

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Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks. INDEX TERMS Sine cosine algorithm, optimization, chaotic local search, Lévy flight.

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Chaos-enhanced synchronized bat optimizer

Cited by 92 publications

References 46 publications

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers

Rationalized Sine Cosine Optimization With Efficient Searching Patterns

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