Discrete Bat Algorithm Research Articles

Multi-Document Summarization using Discrete Bat Optimization

With the World Wide Web, we now have a wide range of data that was previously unavailable. Therefore, it has become a complex problem to find useful information in large datasets. In recent years, text summarization has emerged as a viable option for mining relevant data from massive collections of texts. We may classify summarizing as either "single document" or "multi document" depending on how many source documents we are working with. Finding an accurate summary from a collection of documents is more difficult for researchers than doing it from a single document. For this reason, this research proposes a Discrete Bat algorithm Optimization based multi document summarizer (DBAT-MDS) to tackle the issue of multi document summarizing. Comparisons are made between the proposed DBAT-MDS based model and three different summarization algorithms that take their inspiration from the natural world. All methods are evaluated in relation to the benchmark Document Understanding Conference (DUC) datasets using a variety of criteria, such as the ROUGE score and the F score. Compared to the other summarizers used in the experiment, the suggested method performs much better.

An Improved Discrete Bat Algorithm for Multi-Objective Partial Parallel Disassembly Line Balancing Problem

Product disassembly is an effective means of waste recycling and reutilization that has received much attention recently. In terms of disassembly efficiency, the number of disassembly skills possessed by workers plays a crucial role in improving disassembly efficiency. Therefore, in order to effectively and reasonably disassemble discarded products, this paper proposes a partial parallel disassembly line balancing problem (PP-DLBP) that takes into account the number of worker skills. In this paper, the disassembly tasks and the disassembly relationships between components are described using AND–OR graphs. In this paper, a multi-objective optimization model is established aiming to maximize the net profit of disassembly and minimize the number of skills for the workers. Based on the bat algorithm (BA), we propose an improved discrete bat algorithm (IDBA), which involves designing adaptive composite optimization operators to replace the original continuous formula expressions and applying them to solve the PP-DLBP. To demonstrate the advantages of IDBA, we compares it with NSGA-II, NSGA-III, SPEA-II, ESPEA, and MOEA/D. Experimental results show that IDBA outperforms the other five algorithms in real disassembly cases and exhibits high efficiency.

A discrete bat algorithm for collaborative scheduling of discrete manufacturing logistics

A discrete bat algorithm for collaborative scheduling of discrete manufacturing logistics

A discrete bat algorithm for collaborative scheduling of discrete manufacturing logistics

A discrete bat algorithm for collaborative scheduling of discrete manufacturing logistics

A Multihop Task Offloading Decision Model in MEC-Enabled Internet of Vehicles

As a new network technology, mobile-edge computing (MEC) combined with the Internet of Vehicles (IoV) can effectively improve the efficiency of task computing and offloading. However, the power of edge computing will be severely limited to the areas with poor MEC server coverage. Furthermore, there are a number of peripheral vehicles with temporarily idle computing resources on the road, so how to put the resources of these vehicles into use becomes the primary issue to be considered. In this article, a distributed multihop task offloading decision model for task execution efficiency is developed, which mainly consists of two parts: 1) a candidate vehicle selection mechanism for screening the neighboring vehicles that can participate in offloading and 2) a task offloading decision algorithm for obtaining the task offloading solution. Considering the impact of different hop and wireless communication ranges on communication ranges on task completion in a generic scenario, we introduce the hop count <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and select the neighboring vehicles in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -hop wireless communication range as the candidate vehicles. Then, the problem of offloading is modeled as a generalized allocation model with constraints which is solved by the greedy algorithm and discrete bat algorithm, respectively. The results show that compared with the scheme in which the task vehicle randomly selects the neighboring vehicles to offload and the scheme that all tasks are completed locally, the offloading scheme in which all tasks are completed under the greedy algorithm or bat-based algorithm has advantages in time delay performance in terms of different task number, task required computation power, and task size environment. Besides, this article also explores the influence of hop count <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> on the results when selecting candidate vehicles from the neighboring vehicles within the range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> hop. The results show that the increase of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> will also increase the number of candidate vehicles, which makes the time delay lower. Under the parameters set in this article, the time delay required for the greedy algorithm offloading scheme to complete all tasks is a lower bound on the time delay of the bat algorithm scheme. The greedy algorithm scheme reduces latency by 0.2–2.4 s compared to the scheme where tasks are all completed locally, and it reduces latency by 0.16–2.3 s compared to the random offloading scheme.

A discrete cuckoo search algorithm for traveling salesman problem and its application in cutting path optimization

The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem with various industrial applications. This paper proposes a discrete cuckoo search algorithm based on random walk and cluster analysis to solve the traveling salesman problem (TSP). Although the original cuckoo search algorithm is not suitable to solve the TSP, this algorithm is modified to solve the TSP. Lévy flights and random preference walk in the original cuckoo search algorithm are replaced with novel tools, including the local adjustment operator and discrete random walk. The former is utilized to preserve superior solutions found by the algorithm, while the latter is employed to maintain the diversity of the population. For large-scale TSP problems, the k-means algorithm is first utilized to divide cities into k categories for optimization, while random algorithms are adopted to combine them. A simple 2-opt operator is utilized as the local optimization operator to accelerate the algorithm's convergence rate. Multiple groups of standard TSPLIB datasets are chosen to compare the proposed method with state-of-the-art optimization algorithms that also use the 2-opt/k-opt optimization operator. According to the experimental results, the stability and accuracy of the proposed algorithm are superior to the state-of-the-art optimization algorithms. Regarding the solution accuracy, the algorithm improves 0.39%, 0.29%, 0.84%, 0.4%, 0.07% and 2.22% in the optimal solution over discrete cuckoo search (DCS), discrete bat algorithm (DBA), discrete sine–cosine algorithm (DSCA), random-key cuckoo search (RKCS) and discrete spider monkey optimization (DSMO) on the standard test cases, and 0.58%, 0.19%, 0.85%, 0.43%, 0.36% and 5.83% in the average optimal solution. Regarding stability, the variance of the optimal solutions of RKCS, DSMO, and novel discrete differential evolution (NDEE) for 30 independent runs is 38.5, 52.6, and 59.4 times higher than that of the algorithm in this paper. Finally, the proposed algorithm optimizes air knife migration during glass cutting. Accordingly, the path for air knife migration is reduced by 8556 mm.

Optimized Feature Selection in Software Product Lines using Discrete Bat Algorithm

Software Product Lines (SPLs) are one of the ways to develop software products by increasing productivity and reducing cost and time in the software development process. In SPLs, each product has many features and it is necessary to consider the optimal and custom features of the products. In fact, selecting key features in SPLs is a challenging process. Feature selection in SPLs is an optimization problem and an NP-Hard problem. One way to select a feature is to use meta-heuristic algorithms modeled on nature, i.e., Bat Algorithm. By modeling bat behavior in prey hunting, a suitable meta-innovative algorithm is considered. This algorithm has important advantages that make it more accurate than conventional methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In this paper, to select software product features, idol binary algorithm and artificial neural network are used to identify important features of software products that reduce production costs and time. The experiments in MATLAB software and datasets related to software production lines show that the rate of reduction of target performance error or feature selection cost in software production lines in the proposed method has decreased by 64.17% with increasing population.

Pairwise Biological Network Alignment Based on Discrete Bat Algorithm.

The development of high-throughput technology has provided a reliable technical guarantee for an increased amount of available data on biological networks. Network alignment is used to analyze these data to identify conserved functional network modules and understand evolutionary relationships across species. Thus, an efficient computational network aligner is needed for network alignment. In this paper, the classic bat algorithm is discretized and applied to the network alignment. The bat algorithm initializes the population randomly and then searches for the optimal solution iteratively. Based on the bat algorithm, the global pairwise alignment algorithm BatAlign is proposed. In BatAlign, the individual velocity and the position are represented by a discrete code. BatAlign uses a search algorithm based on objective function that uses the number of conserved edges as the objective function. The similarity between the networks is used to initialize the population. The experimental results showed that the algorithm was able to match proteins with high functional consistency and reach a relatively high topological quality.

Hybrid Chaotic Discrete Bat Algorithm with Variable Neighborhood Search for Vehicle Routing Problem in Complex Supply Chain

Driven by the supply chain, suppliers, manufacturers and warehouses are working more closely together for improving service quality. However, tremendous cost may incur in the supply chain if transportation is not planned properly and efficiently, which frustrates enterprises in the intense market. In this paper, we present a model of vehicle routing problem in complex supply chain (VRPCSC) and propose an intelligent algorithm called hybrid chaotic discrete bat algorithm with variable neighborhood search for minimizing the purchase cost of materials, processing cost, and delivery cost along the path from suppliers, to manufacturers and warehouses in the vehicle routing problem. Based on the principles of bat algorithm, a discrete chaotic initialization strategy (DCIS) and a variable neighborhood search (VNS) are adopted to enhance the convergence capacity. Finally, two sets of experiments are conducted, which show that the proposed algorithm can solve the VRPCSC effectively.

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

Novel Discrete Rao Algorithms for Solving the Travelling Salesman Problem

Rao algorithms are population-based metaphor-less optimization algorithms. Rao algorithms consist of three algorithms characterized by three mathematical equations. These algorithms use the characteristics of the best and worst solution to modify the current population along with some characteristics of a random solution. These algorithms are found to be very efficient for continuous optimization problems. In this paper, efforts are made to convert Rao 1 algorithm to its discrete form. This paper proposes three techniques for converting these continuous Rao algorithms to their discrete form. One of the techniques is based on swap operator used for transforming PSO to discrete PSO, and the other two techniques are based on two novel mutating techniques. The algorithms are applied to symmetric TSP problems, and the results are compared with different state of the art algorithms, including discrete bat algorithm (DBA), discrete cuckoo search (DCS), ant colony algorithm, and GA. The results of Rao algorithms are highly competitive compared to the rest of the algorithms

A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks

The problem of identifying the top-k influential node is still an open and deeply felt issue. The development of a stable and efficient algorithm to deal with such identification is still a challenging research hot spot. Although conventional centrality-based and greedy-based methods show high performance, they are not very efficient when dealing with large-scale social networks. Recently, algorithms based on swarm intelligence are applied to solve the problems mentioned above, and the existing researches show that such algorithms can obtain the optimal global solution. In particular, the discrete bat algorithm (DBA) has been proved to have excellent performance, but the evolution mechanism based on a random selection strategy leads to the optimal solution's instability. To solve this problem, in this paper, we propose a clique-DBA algorithm. The proposed algorithm is based on the clique partition of a network and enhances the initial DBA algorithm's stability. The experimental results show that the proposed clique-DBA algorithm converges to a determined local influence estimation (LIE) value in each run, eliminating the phenomenon of large fluctuation of LIE fitness value generated by the original DBA algorithm. Finally, the simulated results achieved under the independent cascade model show that the clique-DBA algorithm has a comparable performance of influence spreading compared with the algorithms proposed in the state of the art.

A discrete bat algorithm based on Lévy flights for Euclidean traveling salesman problem

Bat algorithm is a swarm-intelligence-based metaheuristic proposed in 2010. This algorithm was inspired by echolocation behavior of bats when searching their prey in nature. Since it first introduction, it continues to be used extensively until today, owing to its simplicity, easy handling and applicability to a wide range of problems. However, sometimes the major challenge faced by this technique is can be trapped in a local optimum when facing large complex problems. In this research work, a new discrete bat algorithm is proposed to solve the famous traveling salesman problem as NP-hard combinatorial optimization problem. To enhance the searching strategy and to avoid getting stuck in local minima, random walks based on Lévy's flights are combined with bat’s movement. In addition, to improve the diversity and convergence of the swarm, a neutral crossover operator is embedded to the proposed algorithm. To evaluate the performance of our proposal, two experiments are conducted on 38 benchmark datasets and the obtained results are compared with eight different approaches. Furthermore, the student’s t-test, the Friedman’s test and the post hoc Wilcoxon's test are performed to check whether there are significant differences between the proposed optimizer and the alternative techniques. The experimental results under comparative studies have shown that, in most cases, the proposed discrete bat algorithm yields significantly better results compared with its competitors.

A novel discrete bat algorithm for heterogeneous redundancy allocation of multi-state systems subject to probabilistic common-cause failure

This paper focuses on a heterogeneous redundancy allocation problem (RAP) for multi-state series-parallel systems subject to probabilistic common-cause failure and proposes a novel discrete bat algorithm to solve it. Although abundant research studies have been published for solving multi-state RAPs, few of them have studied probabilistic common cause failure, which motivates this paper. Due to the insufficient data of components, an interval-valued universal generating function is utilized to evaluate the availability of components and the whole system. The challenge of solving this kind of RAPs lies in not only the reliability estimation, but also the solution method. This paper presents a novel discrete bat algorithm (BA) for effectively dealing with the proposed RAP and alleviating the premature convergence of BA. Two main features of the adaptation are Hamming distance-based bat movement (HDBM) and Q learning-based local search (QLLS). HDBM transfers the Hamming distance between the current bat and the best bat in the swarm to the movement rate. Then, QLLS utilizes Q-learning to adjust the local search strategies dynamically during the iterations. The computational results from extensive experiments demonstrate that the proposed algorithm is powerful, which is more efficient than other state-of-the-arts on this sort of problems.

Multi-point shortest path planning based on an Improved Discrete Bat Algorithm

Multi-point shortest path planning problem is a typical problems in discrete optimization. The bat algorithm is a nature-inspired metaheuristic optimization algorithm that is used in a wide range of fields. However, there is one problem with the BA, which is easy to premature. To solve multi-point shortest path planning problem, an improved discrete bat algorithm (IDBA) is proposed in this paper. In this algorithm, the Floyd–Warshall algorithm is first used to transform an incomplete connected graph into a complete graph whose vertex set consists of a start point and necessary points. Then the algorithm simulates the bats’ foraging and obstacle avoidance process to find the shortest path in the complete graph to satisfy the constraints. Finally, the path is transferred to the original incomplete graph to get the solution. In order to overcome the premature phenomenon of a discrete bat algorithm, the modified neighborhood operator is proposed. To prove the effectiveness of our method, we compared its performance in 26 instances with the results obtained by three different algorithms: DBA, IBA and GSA-ACS-PSOT. We also performed a sensitivity analysis on the parameters. The results indicate that the improved bat algorithm outperforms all the other alternatives in most cases.

Determining Network Communities Based on Modular Density Optimization

Background:In today’s world, complex systems are conceptually observed in the form of network structure. Communities inherently existing in the networks have a recognizable elucidation in understanding the organization of networks. Community discovery in networks has grabbed the attention of researchers from multi-discipline. Community detection problem has been modeled as an optimization problem. In broad-spectrum, existing community detection algorithms have adopted modularity as the optimizing function. However, the modularity is not able to identify communities of smaller size as compared to the size of the network.Methods:This paper addresses the problem of the resolution limit posed by modularity. Modular density measure succeeds in countering the resolution limit problem. Finding network communities with maximum modular density is an NP-hard problem In this work, the discrete bat algorithm with modular density as the optimization function is recommended.Results:Experiments are conducted on three real-world datasets. For determining the consistency, ten independent runs of the proposed algorithm has been carried out. The experimental results show that our proposed algorithm produces high-quality community structure along with small size communities.Conclusion:The results are compared with traditional and evolutionary community detection algorithms. The final outcome shows the superiority of discrete bat algorithm with modular density as the optimization function with respect to number of communities, maximum modularity, and average modularity.

Clustering Social Networks using Nature-inspired BAT Algorithm

The widespread extent of internet availability at low cost impels user activities on social media. As a result, a huge number of networks with a lot of varieties are easily accessible. Community detection is one of the significant tasks to understand the behavior and functionality of such real-world networks. Mathematically, community detection problem has been modeled as an optimization problem and various meta-heuristic approaches have been applied to solve the same. Progressively, many new nature-inspired algorithms have also been explored to handle the diverse optimization problems in the last decade. In this paper, nature-inspired Bat Algorithm (BA) is adopted and a new variant of Discrete Bat algorithm (NVDBA) is recommended to identify the communities from social networks. The recommended scheme does not require the number of communities as a prerequisite. The experiments on a number of real-world networks have been performed to assess the performance of the proposed approach which in turn confirms its validity. The results confirm that the recommended algorithm is competitive with other existing methods and offers promising results for identifying communities in social networks.

A hybrid enhanced bat algorithm for the generalized redundancy allocation problem

A majority of existing works dealing with redundancy allocation problems are based on traditional series-parallel structures. While in many real-life scenarios, the way of connecting subsystems is not limited to a series-only configuration. This paper considers a generalized redundancy allocation problem (GRAP), where the system structure is a more general network. Since the reliability evaluation in GRAPs is a NP-hard problem and the traditional exact symbolic reliability calculation is not suitable, a cellular automata based monte carlo simulation method is implemented in this paper to estimate the system reliability. It is a relatively simple but effective method without knowing the MPs/MCs. Moreover, to deal with GRAPs, a novel discrete bat algorithm is proposed in this paper with a goal of determining an optimal system structure that achieves the minimum cost under several constraints by using redundant components in parallel. Computational complexity of the proposed algorithm is also calculated in this paper. In the end, three experiments are carried out based on ten networks to set parameters, measure the effectiveness of the modifications, and compare with other state-of-the-art algorithms, separately. The reported computational results show that the proposed algorithm is powerful, which is more superior on this sort of problems.

Bi-Population Based Discrete Bat Algorithm for the Low-Carbon Job Shop Scheduling Problem

Job shop scheduling problem (JSP) is a combinatorial optimization problem, which has been widely studied due to its strong theoretical and background for application. However, in previous studies on the traditional JSP, the optimization objective is mainly relative to time, such as makespan, flow time, tardiness, earliness, and workload. With the advent of green manufacturing, energy consumption should be considered in the JSP. Therefore, a low-carbon JSP is studied in this paper. Due to the NP-hard nature, a meta-heuristic algorithm, bat algorithm (BA), is considered in this paper. According to the characteristics of the problem, a kind of bi-population-based discrete BA (BDBA) is proposed to minimize the sum of the energy consumption cost and the completion-time cost. A parallel searching mechanism is first introduced to the algorithm, by which the population is divided into two sub-populations to, respectively, adjust the job permutation and the processing speed of each machine. Three communication strategies are used to implement the cooperation between the sub-populations. In addition, due to the fact that the original BA was developed to deal with the continuous problems, a modified discrete updating approach is proposed to make the BA algorithm directly work in a discrete domain. Finally, extensive simulations have been conducted to test the effectiveness of the proposed BDBA algorithm. The experimental data demonstrate that the proposed BDBA is effective in solving the low-carbon JSP under study.

Chaotic discrete bat algorithm for capacitated vehicle routing problem

Chaotic discrete bat algorithm for capacitated vehicle routing problem