scholarly journals Bacterial Foraging Algorithm for Optimal Joint-Force Searching Strategy of Multi – SAR Vessels at Sea

2020 ◽  
Author(s):  
Ngoc Ha Pham ◽  
Minh Duc Nguyen
Keyword(s):  
Median Filter ◽  
Search And Rescue ◽  
Bacterial Foraging Optimization ◽  
Rescue Operation ◽  
Joint Force ◽  
Search Area ◽  
Bacterial Foraging ◽  
Searching Strategy ◽  
Bacterial Foraging Algorithm ◽  
Quick Search

Enhancing the effectiveness of search and rescue operation at sea is always a duty of utmost importance of the coastal states. The search area for distressed objects can be determined by using Monte Carlo simulation, combined with the Median-Filter. Once the search area has been identified, the success of search and rescue operations depends on the sweeping ability of search and rescue vessel at the probability area of the distress object with the minimum time. This is the important element to the success of the search and rescue operation as it minimizes the risk and cost for Search and rescue team. In this article, the authors study and propose the use of Bacterial Foraging Optimization Algorithm (BFOA) to calculate the optimal search and co-ordination route for many search and rescue vessels in Vietnam sea. The simulation results show that it is quite consistent with reality and BFOA can be effectively applied to determine a quick search.

scholarly journals An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Xiaohui Yan ◽  
Yunlong Zhu ◽  
Hao Zhang ◽  
Hanning Chen ◽  
Ben Niu
Keyword(s):  
Social Learning ◽  
Population Size ◽  
Optimization Algorithm ◽  
Statistic Analysis ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Adaptive Step ◽  
Swarm Intelligence Algorithm ◽  
Bacterial Foraging Optimization Algorithm

Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.

scholarly journals A Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm for Optimized Clustering in Wireless Sensor Network

2019 ◽  
Vol 8 (10) ◽  
pp. 2186-2190
Keyword(s):  
Sensor Networks ◽  
Network Lifetime ◽  
Artificial Bee Colony ◽  
Weather Forecasting ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bee Colony ◽  
Bacterial Foraging Algorithm

The emerging ubiquitous nature of wireless sensor networks has made it suitable and applicable to a diversified number of vital applications that include environment surveillance, health monitoring using implantable sensors, weather forecasting and other plethora of contexts. The critical issues such as computation time, limited memory and energy are more common due to the tiny sized hundred and thousands of sensor nodes existing in the networks. In this context, the network lifetime completely depends on the potential use of available resources. The process of organizing closely located sensor nodes into clusters is convenient for effective management of cluster and improving the lifetime of the complete network. At this juncture, swarm intelligent and evolutionary algorithms the pertains to the problem of NP-complete is determined to achieve a superior optimal solution. In this paper, a Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm-based Optimized Clustering (HABC-BFA-OC) is proposed for achieving enhanced network lifetime in sensor networks. In this proposed HABC-BFA-OC technique, the benefits of Bacterial Foraging Optimization is included for improving the local search potential of ABC algorithm in order to attain maximum exploitation and exploration over the parameters considered for cluster head selection. The simulation experiments of the proposed HABC-BFA-OC technique confirmed an enhanced network lifetime with minimized energy consumptions during its investigation with a different number of sensor nodes.

Bacterial Foraging Algorithm Based on Quantum-Behaved Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 948-954 ◽  
Author(s):  
Ling Li ◽  
Xiong Fa Mai
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Local Minimum ◽  
Random Search ◽  
Particle Swarm ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Simulation Results

Bacterial Foraging Optimization(BFA) algorithm has recently emerged as a very powerful technique for real parameter optimization,but the E.coli algorithm depends on random search directions which may lead to delay in reaching the global solution.The quantum-behaved particle swarm optimization (QPSO) algorithm may lead to possible entrapment in local minimum solutions. In order to overcome the delay in optimization and to further enhance the performance of BFA,a bacterial foraging algorithm based on QPSO(QPSO-BFA) is presented.The new algorithm is proposed to combines both algorithms’ advantages in order to get better optimization values. Simulation results on eight benchmark functions show that the proposed algorithm is superior to the BFA,QPSO and BF-PSO.

Image Segmentation Based on Bacterial Foraging and FCM Algorithm

2013 ◽  
pp. 209-222
Author(s):  
Hongwei Mo ◽  
Yujing Yin
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Algorithm ◽  
Data Sets ◽  
Bacterial Foraging Optimization ◽  
Objective Criterion ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Search Capability ◽  
Bacterial Foraging Optimization Algorithm

This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capability of FCM, the Bacterial Foraging Algorithm was employed to optimize the objective criterion function which is interrelated to centroids in FCM. To evaluate the validation of the composite algorithm, cluster validation indexes were used to obtain numerical results and guide the possible best solution found by BF-FCM. Several experiments were conducted on three UCI data sets. For image segmentation, BF-FCM successfully segmented 8 typical grey scale images, and most of them obtained the desired effects. All the experiment results show that BF-FCM has better performance than that of standard FCM.

scholarly journals Simultaneous Localization and Mapping of Mobile Robot Based on Improved RBPF

2018 ◽  
Vol 160 ◽  
pp. 06002
Author(s):  
Jinging Zhang ◽  
Xiaogang Ruan ◽  
Pengfei Dong ◽  
Jing Zhou
Keyword(s):  
Mobile Robot ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Bacterial Foraging Optimization ◽  
The Real ◽  
Bacterial Foraging ◽  
Operation Speed ◽  
Localization And Mapping ◽  
Bacterial Foraging Algorithm ◽  
Robot Position

The traditional SLAM based on RBPF has the problem of constructing high-precision map which requires large amounts of particles to make the calculation complexity and the phenomenon of particle depletion caused by particle degradation. Aiming at these problems, an improved RBPF particle filter based on adaptive bacterial foraging optimization algorithm and adaptive resampling is proposed for mobile robot SLAM problem. Firstly, the introduction of adaptive bacterial foraging algorithm to RBPF making the distribution of particles before resampling closer to the real situation. Then use the adaptive resampling method makes the newly generated particles closer to the real movement, thereby increasing the robot position estimation accuracy and map creation accuracy. The experimental results show that this method can improve the practicability of the system, reduce the computational complexity, improve the operation speed and get more effective particles while guaranteeing the accuracy of the grid map.

scholarly journals Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm

2020 ◽  
Vol 10 (21) ◽  
pp. 7905
Author(s):  
Mohammed Isam Ismael Abdi ◽  
Muhammad Umer Khan ◽  
Ahmet Güneş ◽  
Deepti Mishra
Keyword(s):  
Path Planning ◽  
Flat Surface ◽  
Optimal Path ◽  
Multiple Robots ◽  
Local Minima ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Time And Energy ◽  
Safe Path

The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.

Image Segmentation Based on Bacterial Foraging and FCM Algorithm

2011 ◽  
Vol 2 (3) ◽  
pp. 16-28
Author(s):  
Hongwei Mo ◽  
Yujing Yin
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Algorithm ◽  
Data Sets ◽  
Criterion Function ◽  
Bacterial Foraging Optimization ◽  
Objective Criterion ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Search Capability

This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capability of FCM, the Bacterial Foraging Algorithm was employed to optimize the objective criterion function which is interrelated to centroids in FCM. To evaluate the validation of the composite algorithm, cluster validation indexes were used to obtain numerical results and guide the possible best solution found by BF-FCM. Several experiments were conducted on three UCI data sets. For image segmentation, BF-FCM successfully segmented 8 typical grey scale images, and most of them obtained the desired effects. All the experiment results show that BF-FCM has better performance than that of standard FCM.

scholarly journals Special methods for performing search operation

2020 ◽  
Vol 2020 (4) ◽  
pp. 23-35
Author(s):  
Evgeniy Romanovich Yapparov ◽  
Viktor Valerievich Alekseev
Keyword(s):  
Search Time ◽  
Search And Rescue ◽  
Sequential Search ◽  
Search Range ◽  
Rescue Operation ◽  
Rescue Services ◽  
Search Area ◽  
Trained Personnel ◽  
Operation Rescue ◽  
Technical Measures

The article gives the analysis of planning measures for searching ship in distress as a tactical method of conducting a search and rescue operation. Rescue services of various ministries, departments and organizations, their search and rescue units equipped with the trained personnel and facilities for conducting rescue operations are involved in the search and rescue process. The specialized centers coordinate the actions of search and rescue operation participants maintaining reliable communication between the participants who ensure the operation. A search and rescue operation is a complex of organizational and technical measures that ensure determining the coordinates of people in distress at sea and their subsequent rescue. Special plans for surveying the search area for a ship in distress are considered in detail: search of a probable course, search by variable courses, search by a curtain, radial search. Schemes for determining the boundaries of the sector of probable movement of a ship in distress, calculating sequential search courses, building a search, calculating the time lying on the course, determining the search sector and the search range are illustrated. There has been stated a specific feature of the search for a ship in distress - the lack of necessary information about the object location. The main tasks are listed and substantiated, the solution of which on the site allows the responsible persons to prepare the maximum effective search and rescue operations. A specific example of solving the problem of the radial search is given, a diagram of the circle of encounters is constructed, the maximum meeting angle of the search range is determined, the maximum search range, the necessary data for search and rescue vessels and the required number of search and rescue vessels are calculated, the maximum possible search time is determined.

scholarly journals Survey of search area of ship in distress

2020 ◽  
Vol 2020 (4) ◽  
pp. 53-60
Author(s):  
Evgeniy Romanovich Yapparov ◽  
Viktor Valerievich Alekseev
Keyword(s):  
Survey Methods ◽  
Search And Rescue ◽  
Maximum Width ◽  
Rescue Operation ◽  
Entire Area ◽  
Detection Range ◽  
Search Area ◽  
First Case ◽  
Different Types ◽  
Time Required

The article describes the different types of maneuvering and search methods that require a clear organization. The scheme of survey of the search area of a ship in distress is considered. There have been illustrated the tactical techniques of search and rescue operations at sea characterized by the fact that the exact location of the ship in distress is unknown. The necessary elements and quantities to be calculated during the search and rescue operation are listed: location of search courses; ratio of the speed and detection range at which a ship in distress can be detected in a specified area of a given width; maximum width of the area to be surveyed; time required to survey the area. The scheme of the survey of the search area is presented. Two survey methods are considered: by a single vessel and by a group of vessels. It is noted that in the first case, the survey will be carried out by the rescue vessel moving over the entire area by longitudinal and transverse tacks in turn; the calculations are given. In the second case, when searching by a group of vessels, the possibilities of surveying an area of greater or lesser width can change. Formulas are presented for calculating the required ratio at which a ship in distress can be detected; the size of the transverse and longitudinal tacks; the maximum width of the area that can be surveyed by a group of vessels involved in the search operation. Using a specific example, the elements of the search and rescue process are calculated.

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