Natural selection methods for artificial bee colony with new versions of onlooker bee

Awadallah, Mohammed A.; Al-Betar, Mohammed Azmi; Bolaji, Asaju La’aro; Alsukhni, Emad Mahmoud; Al-Zoubi, Hassan

doi:10.1007/s00500-018-3299-2

Cited by 52 publications

(12 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, MCSA has been implemented by setting very high values to and . Reports for Subcases 3.2 and 3.3 shown in Table 6 are the comparisons of the proposed ICSA approach and other methods such as conventional Evolution programming (CEP) [1], Fast EP (FEP) [1], improved FEP (IFEP) [1], DE [12], multiplier Lagrange-based genetic algorithm with (GA-MU) [15], QPSO [16], GA-PS-SQP [30], PSO-SQP [32], M -HCLSA [49], IABCA [50], CCSA [59], OSE-CSA [59], SOS [34], MSOS [34], CEA-SQT [38], TSBO [39], IWA [40], and CBA [44]. As observed from the table, ICSA approach obtains better solutions than most methods excluding DE [10], CCSA [59], OSE-CSA [59], SOS [34], MSOS [34], CEA-SQT [38], TSBO [39], IWA [40], and CBA [44], especially M -HCLSA [49] with lower cost, $17,960.97.…”

Section: Obtained Results On Case 3 Considering Four Systems With Sfsmentioning

confidence: 99%

“…In addition to such popular original algorithms and hybrid methods, there are many other original and improved methods that have been applied for solving the considered OLD problem. These methods are Symbiotic organisms search algorithm (SOS) [34] and its modified version (MSOS) [34], teaching activity and learning activity-based optimization (TLBO) [35], chemical reactionbased approach (CRBA) [36], enhanced particle swarm optimization (EPSO) [37], sequential quadratic techniquebased cross entropy approach (CEA-SQT) [38], traverse search-based optimization approach (TSBO) [39], invasive weed approach (IWA) [40], Improved Differential evolution (IDE) [41], immune algorithm using power redistribution IAPR [42], Colonial competitive differential evolution (CCDE) [43], Chaotic Bat algorithm (CBA) [44], Exchange market algorithm (EMA) [45], adaptive search technique algorithm and differential evolution (GRASP-DE) [46], -Modified Bat Algorithm ( -MBA) [47], Tournament-based harmony search algorithm (TBHSA) [48], New Modified -Hill Climbing Local Search Algorithm (M -HCLSA) [49], improved version of artificial bee colony algorithm (IABCA) [50], artificial cooperative search algorithm (ACSA) [51], and ameliorated grey wolf optimization algorithm (AGWOA) [52]. Among these methods, ACSA and AGWOA were the two latest methods, which were applied for OLD and published in early 2019.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Finding Optimal Load Dispatch Solutions by Using a Proposed Cuckoo Search Algorithm

Nguyen

Đại

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Optimal load dispatch (OLD) is an important engineering problem in power system optimization field due to its significance of reducing the amount of electric generation fuel and increasing benefit. In the paper, an improved cuckoo search algorithm (ICSA) is proposed for determining optimal generation of all available thermal generation units so that all constraints consisting of prohibited power zone (PPZ), real power balance (RPB), power generation limitations (PGL), ramp rate limits (RRL), and real power reserve (RPR) are completely satisfied. The proposed ICSA method performance is more robust than conventional Cuckoo search algorithm (CCSA) by applying new modifications. Compared to CCSA, the proposed ICSA approach can obtain high quality solutions and speed up the solution search ability. The ICSA robustness is verified on different systems with diversification of objective functions as well as the considered constraint set. The results from the proposed ICSA method are compared to other algorithms for comparison. The result comparison analysis indicates that the proposed ICSA approach is more robust than CCSA and other existing optimization approaches in finding solutions with significant quality and shortening simulation time. Consequently, it should lead to a conclusion that the proposed ICSA approach deserves to be applied for finding solutions of OLD problem in power system optimization field.

show abstract

Section: Obtained Results On Case 3 Considering Four Systems With Sfsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finding Optimal Load Dispatch Solutions by Using a Proposed Cuckoo Search Algorithm

Nguyen

Đại

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The artificial bees are, indeed, the metaphor for the optimization mechanisms (operators). The bees induce the exploration of different areas of the search space, and a selection process guarantees the exploration of previous experiences [7,37].…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

“…Finally, the pseudocode in Algorithm 2 illustrates the ABC procedure and how its structure is performed [37].…”

Section: Scout Bee Stagementioning

confidence: 99%

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

et al. 2021

View full text Add to dashboard Cite

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

show abstract

“…In the algorithm improvement, the balance between the early global exploration capability and the later local development capability should be considered. Enhanced garden balsam optimization (EGBO) uses flower pollination strategy for the population [23,24]. is flower pollination strategy depends on the strength of the pollination.…”

Section: Enhanced Garden Balsam Optimization (Egbo)mentioning

confidence: 99%

Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Sun

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

show abstract

Natural selection methods for artificial bee colony with new versions of onlooker bee

Cited by 52 publications

References 88 publications

Finding Optimal Load Dispatch Solutions by Using a Proposed Cuckoo Search Algorithm

Finding Optimal Load Dispatch Solutions by Using a Proposed Cuckoo Search Algorithm

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Contact Info

Product

Resources

About