A new bat algorithm based on iterative local search and stochastic inertia weight

2018 ◽  
Vol 104 ◽  
pp. 202-212 ◽  
Author(s):  
Chao Gan ◽  
Weihua Cao ◽  
Min Wu ◽  
Xin Chen
Keyword(s):  
Local Search ◽  
Bat Algorithm ◽  
Inertia Weight

scholarly journals A bat optimization algorithm with moderate orientation and perturbation of trend

2021 ◽  
Vol 15 ◽  
pp. 174830262110084
Author(s):  
Jingsen Liu ◽  
Hongyuan Ji ◽  
Qingqing Liu ◽  
Yu Li
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Random Perturbation ◽  
Bat Algorithm ◽  
Local Extremum ◽  
Global Search ◽  
Deep Mining ◽  
Nonlinear Variation ◽  
Optimal Value ◽  
Search Stage

In order to improve the convergence speed and optimization accuracy of the bat algorithm, a bat optimization algorithm with moderate optimal orientation and random perturbation of trend is proposed. The algorithm introduces the nonlinear variation factor into the velocity update formula of the global search stage to maintain a high diversity of bat populations, thereby enhanced the global exploration ability of the algorithm. At the same time, in the local search stage, the position update equation is changed, and a strategy that towards optimal value modestly is used to improve the ability of the algorithm to local search for deep mining. Finally, the adaptive decreasing random perturbation is performed on each bat individual that have been updated in position at each generation, which can improve the ability of the algorithm to jump out of the local extremum, and to balance the early global search extensiveness and the later local search accuracy. The simulating results show that the improved algorithm has a faster optimization speed and higher optimization accuracy.

Enhancing the dynamic load balancing technique for cloud computing using HBATAABC algorithm

2020 ◽  
Vol 11 (05) ◽  
pp. 2050041
Author(s):  
Arif Ullah ◽  
Nazri Mohd Nawi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Local Search ◽  
Data Center ◽  
Bat Algorithm ◽  
Network Stability ◽  
Cloud Data Center ◽  
Abc Algorithm ◽  
Cloud Data ◽  
Novel Technique

Cloud computing brings incipient transmutations in different fields of life and consists of different characteristics and virtualization is one of them. Virtual machine (VM) is one of the main elements of virtualization. VM is a process in which physical server changes into the virtual machine and works as a physical server. When a user sends data or request for data in cloud data center, a situation can occur that may cause the virtual machines to underload data or overload data. The aforementioned situation can lead to failure of the system or delay the user task. Therefore, appropriate load balancing techniques are required to surmount the above two mentioned problems. Load balancing is a technique utilized in cloud computing for management of the resource by a condition such that a maximum throughput is achieved with slightest reaction time and additionally dividing the traffic between different servers or VM so that it can get data without any delay. For the amelioration of load balancing technique in this study, a novel technique is used which is coalescence of BAT and ABC algorithms both of which are nature-inspired algorithms. When the ABC algorithm local search section changes with BAT algorithm local search section, a second modification takes place in the fitness function of BAT algorithm. The proposed technique is known as HBATAABC algorithm. The novel technique implemented by utilizing transfer strategy policy in VM improves the performance of data allocation system of VM in the cloud data center. To check the performance of the proposed algorithm, three main parameters are used which are network average time, network stability and throughput. The performance of the proposed novel technique is verified and tested with the help of cloudsim simulator. The result shows that the suggested modified algorithm increases performance by 1.30% of network average time, network stability and throughput as compared with BAT algorithm, ABC algorithm and RRA algorithm. Nevertheless, the proposed algorithm is more precise and expeditious as compared with the three models.

scholarly journals A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Leilei Cao ◽  
Lihong Xu ◽  
Erik D. Goodman
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Its Region ◽  
Bat Algorithm ◽  
Greedy Strategy ◽  
Search Mechanism ◽  
Genotype Space

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

scholarly journals Stochastic Local Search Algorithms for Feature Selection: A Review

2021 ◽  
Vol 17 (1) ◽  
pp. 1-10
Author(s):  
Hayder Al-Behadili
Keyword(s):  
Feature Selection ◽  
Local Search ◽  
Bat Algorithm ◽  
Search Algorithms ◽  
Future Research ◽  
Feature Subset ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Bee Colony ◽  
Optimal Feature Subset

In today’s world, the data generated by many applications are increasing drastically, and finding an optimal subset of features from the data has become a crucial task. The main objective of this review is to analyze and comprehend different stochastic local search algorithms to find an optimal feature subset. Simulated annealing, tabu search, genetic programming, genetic algorithm, particle swarm optimization, artificial bee colony, grey wolf optimization, and bat algorithm, which have been used in feature selection, are discussed. This review also highlights the filter and wrapper approaches for feature selection. Furthermore, this review highlights the main components of stochastic local search algorithms, categorizes these algorithms in accordance with the type, and discusses the promising research directions for such algorithms in future research of feature selection.

Adaptive inertia weight Bat algorithm with Sugeno-Function fuzzy search

2020 ◽  
Vol 90 ◽  
pp. 106159 ◽  
Author(s):  
Hafiz Tayyab Rauf ◽  
Sumbal Malik ◽  
Umar Shoaib ◽  
Muhammad Naeem Irfan ◽  
M. Ikramullah Lali
Keyword(s):  
Bat Algorithm ◽  
Fuzzy Search ◽  
Inertia Weight
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