A double-subpopulation variant of the bat algorithm

Luo, Jun; Liu, Li-heng; Xianyi, Wu

doi:10.1016/j.amc.2015.04.034

Cited by 23 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Model hybrids ABC [57] aABC [43,58], adaptive ABC (AABC) [59], vortex search [60], cooperative ABC (CABC) [61,62], cooperative micro-ABC (CMABC) [63], interval cooperative multiobjective ABC (ICMOABC) [62], ABC-PSO [64], multiobjective directed bee colony optimization (MODBCO) [65], Scoutless ABC [35], directed ABC [66,67] ACO [68] ACOR [36], heuristic-PS-ACO (HPSACO) [69], hybrid ACO [70], ACO-PSO [71], PS-ACO [72], ACO-SA [73], MWIS-ACO-LS [74], hybrid ACO (HAntCO) [75], min-max ant System (MMAS) [72,76], GA-ACO-SA [77], self-adaptive ant colonygenetic hybrid [78], GA-ACO [79], ACS [80], greedy ACS [81] BA [82] Binary BA [83], hybrid BA with ABC [84], BA-HS [85], adaptive BA [86], adaptive multiswarm BA (AMBA) [87], binary BA [83], differential operator & Levy flights BA [87], directed artificial BA (DABA) [88], double-subpopulation Levy flight BA (DLBA) [89], dynamic virtual BA (DVBA) [90], improved DVBA with probabilistic selection [91], island multipopulational parallel BA (IBA)…”

Section: Modelmentioning

confidence: 99%

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

Chopra

Dhiman

Sharma

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.

show abstract

Section: Modelmentioning

confidence: 99%

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

Chopra

Dhiman

Sharma

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Constraints (18) and (19) define the Boolean type of the emergency supply allocation variable and the path selection variable. Constraints (20), (21), (22), and (23) illustrate the nonnegative nature of these variables and the integer requirements of the transport variables.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Jordehi also proposed a chaotic-based bat algorithm, which can diversify the bats and mitigate premature convergence problem by the ergodicity and nonrepetitious nature of chaotic functions [21]. Jun et al introduced a double subgroup with a dynamic transition strategy into the standard BA to improve global exploring ability [22]. This double-subpopulation Lévy flight bat algorithm (DLBA) outperforms other algorithms in most of their experiments.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] The HSABA for Emergency Location‐Routing Problem

Luan

Yang

Zhou

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This article presents a Location-Routing Problem (LRP) model to assist decision makers in emergency logistics. The model attempts to consider the relationship between the location of warehouses and the delivery routes in order to maximize the rescue efficiency. The objective function of the minimization of time and cost is established in the single-stage LRP model considering different scenarios. The hybrid self-adaptive bat algorithm (HSABA) is an improved nature-inspired algorithm for solving this LRP model, hard optimization problem. The HSABA with self-adaptation mechanism and hybridization mechanism effectively improves the defect of the original BA, that is, trapping into the local optima easily. An example is provided to prove the effectiveness of our model. The studied example shows that the single-stage LRP model can effectively select supply locations and plan rescue routes faced with different disasters and the HSABA outperforms the basic BA.

show abstract

“…They tested it on 10 benchmark functions and found that the proposed algorithm could be used effectively in continuous optimization. Jun et al 22 developed the double‐subpopulation variant for increasing local and global search capability in the standard bat algorithm. They used two subgroups, namely, the external subgroup and the internal subgroup.…”

Section: Related Workmentioning

confidence: 99%

A modified farmland fertility algorithm for solving constrained engineering problems

Gharehchopogh

Farnad

Alizadeh

2021

Concurrency and Computation

View full text Add to dashboard Cite

Solving constrained engineering optimization problems is a highly significant issue, and many different approaches have been proposed in this regard. In this article, a modified farmland fertility algorithm (FFA) has been proposed. This algorithm improves new solutions by benefiting from neighborhoods produced by the new method. In the proposed algorithm, the FFA algorithm phases are modified in the update functions, and some of its variables are replaced with new ones. In the first phase of the algorithm, a mutation is also used to improve the solution of the problem with a particular rule. The results on CEC2019 standard functions were examined to determine the impact of the new parameters. Experiments were performed on 26 standard functions and two constrained engineering optimization problems. To make a better comparison, the analysis of variance, without parameters such as the Friedman test and Pairwise test, was used to evaluate and compare the algorithms. These experiments showed that the proposed algorithm is capable of high‐speed convergence and can minimize most standard functions and engineering constraints within the minimum time and the least function evaluation to the optimum value. The proposed FFA could outperform the previous version of the FFA with minimum modification in convergence, CPU‐time, and complexity.

show abstract

A double-subpopulation variant of the bat algorithm

Cited by 23 publications

References 27 publications

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

[Retracted] The HSABA for Emergency Location‐Routing Problem

A modified farmland fertility algorithm for solving constrained engineering problems

Contact Info

Product

Resources

About