A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

Ruan, Xiangyan; Wang, Jiaming; Zhang, Xu; Liu, Wei-Ting; Fu, Xin

doi:10.3390/app10103352

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Many intelligent algorithms have applied the global-best strategy with desirable results 24 , 25 . The global-best strategy was introduced into the BSO algorithm for the first time 15 .…”

Section: Cogbsomentioning

confidence: 99%

Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning

Zhao,

Cheng,

Cai

et al. 2024

Sci Rep

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Recently, the following global-best strategy and discussion mechanism have been prevailing to solve the slow convergence and the low optimization accuracy in the brain storm optimization (BSO) algorithm. However, the traditional BSO algorithm also suffers from the problem that it is easy to fall into local optimum. Therefore, this work innovatively designed the chaotic difference step strategy. This strategy introduced four commonly used chaotic maps and difference step to expand the population search space to improve the situation. Moreover, opposition-based learning thought was innovatively adopted into the BSO algorithm. The thought aims to generate the opposition-based population, increase the search density, and make the algorithm out of the local optimum as soon as possible. In summary, this work proposed a global-best brain storm optimization algorithm based on the chaotic difference step and opposition-based learning (COGBSO). According to the CEC2013 benchmark test suit, 15 typical benchmark functions were selected, and multiple sets of simulation experiments were conducted on MATLAB. The COGBSO algorithm was also compared to recent competitive algorithms based on the complete CEC2018 benchmark test suit. The results demonstrate that the COGBSO outperforms BSO and other improved algorithms in solving complex optimization problems.

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“…Many intelligent algorithms have applied the global-best strategy with desirable results 24 , 25 . The global-best strategy was introduced into the BSO algorithm for the first time 15 .…”

Section: Cogbsomentioning

confidence: 99%

Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning

Zhao,

Cheng,

Cai

et al. 2024

Sci Rep

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“…Constraints ( 26) and (27) ensure that the box's rotation value is a binary variable, i.e., {0, 1}. Constraints ( 28)- (30) state that the box and knapsack start points are at (0,0,0), and Constraint (31) guarantees that the total sum of the weights of all chosen boxes is less than or equal to the knapsack weight. Finally, Constraints ( 32)-( 36) specify the binary variables.…”

Section: Mathematical Model For Solving Multi-objective Three-dimensi...mentioning

confidence: 99%

“…Thus, in this research, we combine the artificial bee colony algorithm with a Paretobased approach to solve the SCLP for strongly heterogeneous rectangular boxes. The ABC algorithm is commonly used by researchers because of its simplicity, flexibility, robustness, ability to explore local solutions, ability to handle objective costs, and broad applicability with complex functions [31]. However, one of its well-known limitations is its tendency to get trapped in local optima, which is ascribed to its exploration process.…”

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony Algorithm with Pareto-Based Approach for Multi-Objective Three-Dimensional Single Container Loading Problems

et al. 2023

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The ongoing container shortage crisis has presented significant challenges for the freight forwarding industry, requiring companies to implement adaptive measures in order to maintain peak operational efficiency. This article presents a novel mathematical model and artificial bee colony algorithm (ABC) with a Pareto-based approach to solve single-container-loading problems. The goal is to fit a set of boxes with strongly heterogeneous boxes into a container with a specific dimension to minimize the broken space and maximize profits. Furthermore, the proposed algorithm incorporates the bottom-left fill method, which is a heuristic strategy for packing containers. We conducted numerical testing to identify optimal parameters using the C~ metric method. Subsequently, we evaluated the performance of our proposed algorithm by comparing it to other heuristics and meta-heuristic approaches using the relative improvement (RI) value. Our analysis showed that our algorithm outperformed the other approaches and achieved the best results. These results demonstrate the effectiveness of the proposed algorithm in solving real-world single-container-loading problems for freight forwarding companies.

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“…A linear control surface would be consistent with a plane, and while the control surface above exhibits some near-linear behavior in the central section of the surface, non-linear behavior is evident at extreme values of rate and time and by «bumpy» and sharp-changing regions throughout. There are three primary sources for the non-linear surface in an FLC [17,20]. The experiment has been divided into two parts.…”

Section: Mechanism Flc Controlling On Switching In the Hef Algorithmmentioning

confidence: 99%

Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

Afathi

2021

EEJET

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The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

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A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

Cited by 6 publications

References 28 publications

Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning

Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning

Artificial Bee Colony Algorithm with Pareto-Based Approach for Multi-Objective Three-Dimensional Single Container Loading Problems

Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

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