ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment

Issa, Mohamed; Hassanien, Aboul Ella; Oliva, Diego; Helmi, Ahmed; Ziedan, Ibrahim; Alzohairy, Ahmed M.

doi:10.1016/j.eswa.2018.01.019

Cited by 130 publications

(52 citation statements)

References 36 publications

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“…In the updating strategy of SCA, sine and cosine functions in Equation (23) possess the outstanding ability of exploration but also have some drawbacks, such as slow convergence and the likelihood of being trapped in a local optimum. The reason for the above problems is that parameters of SCA are difficult to set properly, so a poor combination of parameters will result in weak exploitation, but its exploration phase will not be affected [34]. On the other hand, the PSO algorithm has the advantages of information exchange among particles and good robustness, which lead to the high probability of exploiting local optimal solution.…”

Section: Mutation Sca-pso Optimizationmentioning

confidence: 99%

“…Step 4: Update si best in top layer based on Equations (27) and 28; In iterations, the optimal value found by each group in the bottom layer is saved by a top layer particle. Through this operation, the top layer particles focus on exploitation by using advantages of PSO, while the bottom layer individuals focus on exploration, giving play to advantages of SCA [34]. The updating strategy of bottom layer individuals is changed to the following equation:…”

Section: Mutation Sca-pso Optimizationmentioning

confidence: 99%

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Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Tan

et al. 2019

Entropy

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Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.

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Section: Mutation Sca-pso Optimizationmentioning

confidence: 99%

Section: Mutation Sca-pso Optimizationmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Tan

et al. 2019

Entropy

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“…But this kind of exploitation mechanism makes it prone to stagnation in a local optimum in multi-objective optimization. The sine cosine algorithm (SCA) [40], which has been widely applied in the fields of engineering [41][42][43], computer science [44][45][46], control system [47,48], energy [49][50][51], and instrument [52,53], requires the solutions to fluctuate towards or outwards of the true Pareto optimal front based on the sine cosine function. SCA is superior in balancing exploration and exploitation, which makes it able to approximate the true Pareto optimal front.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm

Zhao

Wang

et al. 2020

Energies

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Construction duration and schedule robustness are of great importance to ensure efficient construction. However, the current literature has neglected the importance of schedule robustness. Relatively little attention has been paid to schedule robustness via deviation of an activity’s starting time, which does not consider schedule robustness via structural deviation caused by the logical relationships among activities. This leads to a possibility of deviation between the planned schedule and the actual situation. Thus, an optimization model of construction duration and schedule robustness is proposed to solve this problem. Firstly, duration and two robustness criteria including starting time deviation and structural deviation were selected as the optimization objectives. Secondly, critical chain method and starting time criticality (STC) method were adopted to allocate buffers to the schedule in order to generate alternative schedules for optimization. Thirdly, hybrid grey wolf optimizer with sine cosine algorithm (HGWOSCA) was proposed to solve the optimization model. The movement directions and speed of grey wolf optimizer (GWO) was improved by sine cosine algorithm (SCA) so that the algorithm’s performance of convergence, diversity, accuracy, and distribution improved. Finally, an underground power station in China was used for a case study, by which the applicability and advantages of the proposed model were proved.

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“…e third is to improve the search performance by changing PSO's particle topology. Issa et al [28] conducted the ground search in accordance with the concept of hierarchy and the favorable exploitation of prominent regions of sine cosine optimization (SCA) so as to improve the accuracy of search. It conducted the top search using the excellent exploration of the search space of PSO to increase the population diversity.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the randomness of the initial population, the grouping strategy will inevitably affect the efficiency of the algorithm. is problem is not the focus of literature [28], and there is less discussion on the updating method of parameters in SCA.…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Holonic Particle Swarm Optimization

Li¹,

Jin²,

Wang³

et al. 2019

Mathematical Problems in Engineering

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For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. e HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. erefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. e performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. erefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. e HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. is paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.

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ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment

Cited by 130 publications

References 36 publications

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm

Improved Adaptive Holonic Particle Swarm Optimization

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