Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times

Jouhari, Hamza; Lei, Deming; Al-qaness, Mohammed A. A.; Elaziz, Mohamed Abd; Ewees, Ahmed A.; Farouk, Osama

doi:10.3390/math7111120

Cited by 43 publications

(28 citation statements)

References 45 publications

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“…In this experiment, we compare the results of δ of MHHO with other methods using small-size problems as in Table 3. These methods including the traditional HHO, and SSA, SA [75], T9 [74], T8 [74]. From these results, it can be observed that the MHHO outperforms other methods, especially the SA, T9, and T8.…”

Section: Results Of δ Over Small-size Problemsmentioning

confidence: 99%

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Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

et al. 2020

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Scheduling can be described as a decision-making process. It is applied in various applications, such as manufacturing, airports, and information processing systems. More so, the presence of symmetry is common in certain types of scheduling problems. There are three types of parallel machine scheduling problems (PMSP): uniform, identical, and unrelated parallel machine scheduling problems (UPMSPs). Recently, UPMSPs with setup time had attracted more attention due to its applications in different industries and services. In this study, we present an efficient method to address the UPMSPs while using a modified harris hawks optimizer (HHO). The new method, called MHHO, uses the salp swarm algorithm (SSA) as a local search for HHO in order to enhance its performance and to decrease its computation time. To test the performance of MHHO, several experiments are implemented using small and large problem instances. Moreover, the proposed method is compared to several state-of-art approaches used for UPMSPs. The MHHO shows better performance in both small and large problem cases.

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Section: Results Of δ Over Small-size Problemsmentioning

confidence: 99%

“…Equation 1is the fitness function of UPMS problems that requires minimizing the value of C max . More details can be found in [74,75].…”

Section: Problem Definitionmentioning

confidence: 99%

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

et al. 2020

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“…Moreover, the ABC algorithm was effectively applied on several MTS tasks, it suffers from some drawbacks, such as taking more time to explore the population, requiring many numbers of iterations to reach the optimal solution, and can get stuck in local minima, therefore, in this paper the ABC is improved using the Sine-Cosine Algorithm (SCA) to minimize these drawbacks. The SCA algorithm has many advantages such as low computation requirement and few predefined parameters that provide stable results; it had been applied to solve several problems, for instance [19]- [24].…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation

et al. 2020

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Multilevel-thresholding is an efficient method used in image segmentation. This paper presents a hybrid meta-heuristic approach for multi-level thresholding image segmentation by integrating both the artificial bee colony (ABC) algorithm and the sine-cosine algorithm (SCA). The proposed algorithm, called ABCSCA, is applied to segment images and it utilizes Otsu's function as the objective function. The proposed ABCSCA uses ABC to optimize the threshold and to reduce the search region. Thereafter, the SCA algorithm uses the output of ABC to determine the global optimal solution, which represents the thresholding values. To evaluate the performance of the proposed ABCSCA, a set of experimental series is performed using nineteen images. In the first experimental series, the proposed ABCSCA is assessed at the low threshold levels and compared with the ABC and SCA as traditional methods. Moreover, the second experimental series aims to evaluate the ABCSCA at high threshold levels and it is compared with six algorithms in addition to the SCA and ABC. Besides, the proposed method is evaluated using the fuzzy entropy. The results demonstrate the effectiveness of the proposed algorithm and showed that it outperforms other algorithms in terms of performance measures, such as Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM).INDEX TERMS Image segmentation, multi-level thresholding, artificial bee colony (ABC), sine-cosine algorithm (SCA).

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“…The SCA is employed to optimize the parameters of the ANFIS. In Reference [ 31 ], the authors applied SCA to enhance simulated annealing (SA) algorithm to build an efficient model for scheduling jobs in unrelated parallel machines that can be employed in manufacturing scheduling applications. In Reference [ 32 ], the SCA is applied to enhance the artificial bee colony (ABC) that applied for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

Alwerfali

Al-qaness

Elaziz

et al. 2020

Entropy

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Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

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Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times

Cited by 43 publications

References 45 publications

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

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