An efficient and robust bat algorithm with fusion of opposition-based learning and whale optimization algorithm

2020 ◽  
Vol 24 (3) ◽  
pp. 581-606 ◽  
Author(s):  
Jinkun Luo ◽  
Fazhi He ◽  
Jiashi Yong
Keyword(s):  
Optimization Algorithm ◽  
Bat Algorithm ◽  
Whale Optimization Algorithm ◽  
Opposition Based Learning ◽  
Whale Optimization

scholarly journals A Combination Approach of Two Metaheuristic Algorithm for Optimal Feature Selection: Case Study Email Spam Detection

2020 ◽  
Author(s):  
Hekmat Mohmmadzadeh
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Selection Problem ◽  
Second Step ◽  
Feature Selection Problem ◽  
Flower Pollination ◽  
Opposition Based Learning ◽  
Whale Optimization

Selecting a feature in data mining is one of the most challenging and important activities in pattern recognition. The issue of feature selection is to find the most important subset of the main features in a specific domain, the main purpose of which is to remove additional or unrelated features and ultimately improve the accuracy of the categorization algorithms. As a result, the issue of feature selection can be considered as an optimization problem and to solve it, meta-innovative algorithms can be used. In this paper, a new hybrid model with a combination of whale optimization algorithms and flower pollination algorithms is presented to address the problem of feature selection based on the concept of opposition-based learning. In the proposed method, we tried to solve the problem of optimization of feature selection by using natural processes of whale optimization and flower pollination algorithms, and on the other hand, we used opposition-based learning method to ensure the convergence speed and accuracy of the proposed algorithm. In fact, in the proposed method, the whale optimization algorithm uses the bait siege process, bubble attack method and bait search, creates solutions in its search space and tries to improve the solutions to the feature selection problem, and along with this algorithm, Flower pollination algorithm with two national and local search processes improves the solution of the problem selection feature in contrasting solutions with the whale optimization algorithm. In fact, we used both search space solutions and contrasting search space solutions, all possible solutions to the feature selection problem. To evaluate the performance of the proposed algorithm, experiments are performed in two stages. In the first phase, experiments were performed on 10 sets of data selection features from the UCI data repository. In the second step, we tried to test the performance of the proposed algorithm by detecting spam emails. The results obtained from the first step show that the proposed algorithm, by running on 10 UCI data sets, has been able to be more successful in terms of average selection size and classification accuracy than other basic meta-heuristic algorithms. Also, the results obtained from the second step show that the proposed algorithm has been able to perform spam emails more accurately than other similar algorithms in terms of accuracy by detecting spam emails.

scholarly journals Case Study Email Spam Detection of Two Metaheuristic Algorithm for Optimal Feature Selection

2020 ◽  
Author(s):  
Hekmat Mohmmadzadeh
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Selection Problem ◽  
Second Step ◽  
Feature Selection Problem ◽  
Flower Pollination ◽  
Opposition Based Learning ◽  
Whale Optimization

Selecting a feature in data mining is one of the most challenging and important activities in pattern recognition. The issue of feature selection is to find the most important subset of the main features in a specific domain, the main purpose of which is to remove additional or unrelated features and ultimately improve the accuracy of the categorization algorithms. As a result, the issue of feature selection can be considered as an optimization problem and to solve it, meta-innovative algorithms can be used. In this paper, a new hybrid model with a combination of whale optimization algorithms and flower pollination algorithms is presented to address the problem of feature selection based on the concept of opposition-based learning. In the proposed method, we tried to solve the problem of optimization of feature selection by using natural processes of whale optimization and flower pollination algorithms, and on the other hand, we used opposition-based learning method to ensure the convergence speed and accuracy of the proposed algorithm. In fact, in the proposed method, the whale optimization algorithm uses the bait siege process, bubble attack method and bait search, creates solutions in its search space and tries to improve the solutions to the feature selection problem, and along with this algorithm, Flower pollination algorithm with two national and local search processes improves the solution of the problem selection feature in contrasting solutions with the whale optimization algorithm. In fact, we used both search space solutions and contrasting search space solutions, all possible solutions to the feature selection problem. To evaluate the performance of the proposed algorithm, experiments are performed in two stages. In the first phase, experiments were performed on 10 sets of data selection features from the UCI data repository. In the second step, we tried to test the performance of the proposed algorithm by detecting spam emails. The results obtained from the first step show that the proposed algorithm, by running on 10 UCI data sets, has been able to be more successful in terms of average selection size and classification accuracy than other basic meta-heuristic algorithms. Also, the results obtained from the second step show that the proposed algorithm has been able to perform spam emails more accurately than other similar algorithms in terms of accuracy by detecting spam emails.

scholarly journals Opposition-Based Multi-Objective Whale Optimization Algorithm with Multi-Leader Guiding

2021 ◽  
Author(s):  
Yang Li ◽  
Wei-gang Li ◽  
Yun-tao Zhao ◽  
Ao Liu
Keyword(s):  
Optimization Algorithm ◽  
Pareto Front ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Benchmark Problems ◽  
Initial Population ◽  
Multi Objective ◽  
Opposition Based Learning ◽  
Whale Optimization

Abstract Over the years, heuristic algorithms have been widely studied, especially in multi-objective optimization problems (MOPs). The multi-objective whale optimization algorithm based on multi-leader guiding (MOWOAMLG) is proposed in this paper, which is the multi-objective version of whale optimization algorithm (WOA). The proposed algorithm adopts several improvements to enhance optimization performance. First, multiple leadership solutions guide the population to search the sparse space to achieve more homogeneous exploration in per iteration, and the leadership solutions are selected on the Pareto front by grid mechanism and the principle of maximum crowding distance. Second, the differential evolution (DE) is employed to generate the offspring for the leadership solutions, while WOA is employed for the ordinary solutions. In addition, a novel opposition-based learning (OBL) strategy is developed to improve the distribution of the initial population. To show the significance of the proposed algorithm, it is tested on the 20 bi-objective and tri-objective unconstrained benchmark problems of varying nature and complexities. The result of numerical experiments shows that the proposed algorithm has competitive advantages in convergence and distribution while compared with other 10 classic or state-of-the-arts algorithms. The convergence curve of IGD indicates that MOWOAMLG is able to obtain good Pareto front in cost of fewer optimization iterations. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned results have indicated that MOWOAMLG is comparatively effective and efficient to solve MOPs.

scholarly journals A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm

2020 ◽  
Author(s):  
Ashish Kumar Tripathi ◽  
Himanshu Mittal ◽  
Pranav Saxena ◽  
Siddharth Gupta
Keyword(s):  
Optimization Algorithm ◽  
Large Scale ◽  
Recommendation System ◽  
Computation Time ◽  
Bat Algorithm ◽  
Experimental Results ◽  
Whale Optimization Algorithm ◽  
Map Reduce ◽  
Clustering Methods ◽  
Whale Optimization

Abstract In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.

scholarly journals A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-25 ◽  
Author(s):  
Hardi M. Mohammed ◽  
Shahla U. Umar ◽  
Tarik A. Rashid
Keyword(s):  
Optimization Algorithm ◽  
Meta Analysis ◽  
Bat Algorithm ◽  
Whale Optimization Algorithm ◽  
Exploration And Exploitation ◽  
Whale Optimization ◽  
A New Technique ◽  
Nature Inspired Algorithms ◽  
Statistical Results ◽  
Better Than

The whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC and PSO. Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta-analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey’s results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

Sign in / Sign up

Export Citation Format

Share Document