A Novel Coupling Algorithm Based on Glowworm Swarm Optimization and Bacterial Foraging Algorithm for Solving Multi-Objective Optimization Problems

Wang, Yechuang; Cui, Zhihua; Li, Wuchao

doi:10.3390/a12030061

Cited by 12 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then the best neighbor is determined from among this neighborhood group, by calculating the probabilities of all neighbors and this is done using the following equation [32].…”

Section: Glowworm Swarm Optimization (Gso)mentioning

confidence: 99%

Designing a Tool to Estimate Software Projects Based on The Swarm Intelligence

Khaleel¹

2021

IJIES

View full text Add to dashboard Cite

Estimating models in software engineering are used to estimate some important and future characteristics of the software project, such as estimating the developed project effort, and that failure in the program is mainly due to wrong project management practices, so estimating the software effort is a very important step in the software management process for large projects. In this research, a software estimation tool was built to find an efficient and accurate method for estimating the effort. The average COnstructive COst MOdel COCOMO was used, which is classified as one of the best traditional methods among arithmetic estimation models. Four methods of swarm intelligence were used, the first of which is the Glowing worm Swarm Optimization GSO method and the second is the Bird Swarm Algorithm BSA and the third is the first proposed method hybrid BSA-GSO Method1 BSA-GSOM1, where the GSO and BSA algorithms were hybridized, and the performance of the third method was improved to form the fourth method, represented by the second hybrid new method, which called hybrid BSA-GSO Method2 BSA-GSOM2. The new tool was implemented with all its methods on the NASA data set and satisfactory results were obtained by the first and second swarms intelligence, and excellent results were obtained in the first proposed method, but the results of the second proposed method were better and more accurate than the previous ones. many measurements of performance were used for all the methods, the second proposed method yielded the best results from everyone.

show abstract

“…Then the best neighbor is determined from among this neighborhood group, by calculating the probabilities of all neighbors and this is done using the following equation [32].…”

Section: Glowworm Swarm Optimization (Gso)mentioning

confidence: 99%

Designing a Tool to Estimate Software Projects Based on The Swarm Intelligence

Khaleel¹

2021

IJIES

View full text Add to dashboard Cite

show abstract

“…The head node selection has become a popular optimization problem, which influence not only the location but also the limitation of energy, which should be considered in node selection. A biological heuristic algorithm is an effective method for solving this optimization problem such as CS, (BA), GSO, BFO, PSO ABC, etc.…”

Section: Related Workmentioning

confidence: 99%

An improved energy‐low clustering hierarchy protocol based on ensemble algorithm

Xie

Liu

Zhao

2019

Concurrency and Computation

View full text Add to dashboard Cite

Summary LEACH (low‐energy adaptive clustering hierarchy) protocol is an important part of wireless sensor network (WSN) and consisting of a good deal of energy‐constrained nodes. LEACH is widely used but also faces various challenges and constraints. The biggest constraint is that the energy is limited and the network life is finite. In the article, an improved ensemble algorithm based on energy‐efficient LEACH is proposed. The ensemble algorithm benefits from the cuckoo search algorithm (CS), glowworm swarm optimization (GSO), and bacterial foraging optimization (BFO). The paper enhances local and global search capabilities by modify the CS GSO and BFO algorithm. To verify results, this paper performs statistical comparatives with oriented cuckoo search (OCS), bat algorithm with characteristics of Levy flights, particle swarm optimization, and adaptive differential evolution algorithm. Comparison results shows that the new method is effective. Moreover, the research also showed the universal applicability of the ensemble strategy in solving diverse problems by using above different optimization algorithms. To further exploit our mind, this paper applies the improved coupling algorithm to LEACH for selecting the cluster‐head node. Computational experiments illustrate that the method proposed in this paper can provide very competitive results compared to these bio‐inspired LEACH, both in terms of reducing the network energy consumption and improving the lifecycle of nodes.

show abstract

“…Bacterial foraging algorithms are widely used, such as image segmentation, path planning, power system, parameter optimization and identification. The improvement of bacterial foraging in the existing research mainly focuses on the chemotaxis and dispersion of bacteria, as shown in the reference (Hu et al, 2020), (Chen et al, 2017), (Wang et al, 2019), (Ramaporselvi and Geetha 2021) and (Devi and Srinivasan 2021). The contributions of this paper mainly focus on the improvement of the three key steps of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on electrical load distribution using an improved bacterial foraging algorithm

Lv¹

2023

Front. Energy Res.

View full text Add to dashboard Cite

This paper proposes an improved bacterial foraging algorithm for electrical load distribution to impro-ve power plants’ efficiency and reduce energy consumption costs. In the chemotaxis stage, the adaptive step size is introduced to accelerate the random search speed compared with the traditional algorithm. In the replication stage, a hybrid crisscross operator is proposed to replace the traditional binary replication method in the algorithm to ensure the diversity of the population and improve the efficiency of the algorithm. The adaptive dynamic probability is used instead of the initial fixed probability to improve the global search performance of the algorithm. The mathematical model of electrical load distribution in a natural power plant is established, and the improved bacterial foraging algorithm is used to solve the model. Through comparative analysis of two power plant unit experiments, it is proved that the results of the improved algorithm can reduce 3.671% and 1.06% respectively compared with the particle swarm optimization algorithm, and 7.26% and 1.37% respectively compared with the traditional bacterial foraging algorithm, which can significantly reduce the coal consumption of the power plant.

show abstract

A Novel Coupling Algorithm Based on Glowworm Swarm Optimization and Bacterial Foraging Algorithm for Solving Multi-Objective Optimization Problems

Cited by 12 publications

References 31 publications

Designing a Tool to Estimate Software Projects Based on The Swarm Intelligence

Designing a Tool to Estimate Software Projects Based on The Swarm Intelligence

An improved energy‐low clustering hierarchy protocol based on ensemble algorithm

Research on electrical load distribution using an improved bacterial foraging algorithm

Contact Info

Product

Resources

About