Hybrid Bacterial Foraging Optimization Algorithm Research Articles

Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm

The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths.

A hybrid bacterial foraging – simulated annealing framework for improving road networks

Effective techniques for reducing traffic congestion include adding new edges to the road network or increasing the capacity of existing edges. To determine the optimal set of improvements that can be made to an existing road network, it is standard practise to solve a Discrete Network Design Problem. Due to the bi-level nature of its formulation, it is one of the most difficult problems to solve to optimality. The Bacterial Foraging Optimization technique has been investigated in a previous work to gauge its effectiveness in determining the n-optimal solution to the Discrete Network Design Problem. This concept is further developed in this paper by hybridizing the Bacterial Foraging Optimization algorithm with Simulated Annealing. In the elimination step, the metropolis criterion from Simulated Annealing ensures a more diverse search of the solution space. We also employ a modified candidate set of improvements that incorporates both edge and capacity additions. The effectiveness of the hybrid bacterial foraging optimization algorithm in determining n-optimal solutions to the Discrete Network Design Problem has been demonstrated by testing it on the 24-node, 76-arc Sioux Falls network.

Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

The increasing number of electric vehicles (EVs) in today’s transport sector is gradually leading to the phasing out of petroleum-based vehicles. However, the rapid deployment of EVs largely depends on the coordinated and fast expansion of EV charging stations (EVCSs). The integration of EVCSs in the modern distribution network characterized by increased penetration of randomly distributed photovoltaic (PV) systems is challenging as they can lead to excessive power losses and voltage deviations beyond acceptable limits. In this paper, a hybrid bacterial foraging optimization algorithm and particle swarm optimization (BFOA-PSO) technique is proposed for the optimal placement of EVCSs into the distribution network with high penetration of randomly distributed rooftop PV systems. The optimization problem is formulated as a multi-objective problem minimizing active and reactive power losses, average voltage deviation index, and maximizing voltage stability index. The IEEE 69 node distribution network is used as the case network. The simulation is done using MATLAB to integrate the EVCSs in five cases of randomly sized and placed PV systems in the distribution network. For all five cases, a minimal increase in power losses is recorded with minor changes in the voltage deviation and stability indices due to the placement of the EVCSs. But for the voltages of nodes 29 to 48, the other node voltages remain unchanged upon placement of the EVCSs. The largest increase in power losses due to the EVCSs being brought into the network with PVs was noticed in case 3 (from 142.27kW, and 62.90kVar to 147.65kW, and 72.48kVar).

Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility

The flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem (JSP) in which operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled sequencing flexibility, is studied in this work, which considers precedence between the operations in the form of a directed acyclic graph instead of a sequential order. In this work, a mixed integer linear programming (MILP) formulation is presented to minimize weighted tardiness for the FJSP with sequencing flexibility. Due to the NP-hardness of the problem, a novel biomimicry hybrid bacterial foraging optimization algorithm (HBFOA) is developed, which is inspired by the behavior of E. coli bacteria in its search for food. The developed HBFOA search method is hybridized with simulated annealing (SA). Additionally, the algorithm has been enhanced by a local search method based on the manipulation of critical operations. Classical dispatching rules have been employed to create the initial swarm of HBFOA, and a new dispatching rule named minimum number of operations has been devised. The developed approach has been packaged in the form of a decision support system (DSS) developed on top of Microsoft Excel—a tool most small and mid-range enterprises (SME) use heavily for planning. A case study with local industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments using literature benchmarks are further used for validation. The results demonstrate that the HBFOA outperformed the classical dispatching rules and the best integer solution of MILP when minimizing the weighted tardiness and offered comparable results for the makespan instances.

2-D histogram Based Multilevel Thresholding for Image Segmentation by Hybrid Bacterial Foraging Optimization and Particle Swarm Optimization

The objective of image segmentation is to extract meaningful clusters in given image. Meaningful clusters are possible with perfect threshold values which are optimized by assuming Renyi entropy as an objective function. A 1-D histogram based multilevel thresholding is computationally complex and segmented image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. To overcome the problem, a 2-D histogram based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel Hybrid Bacterial Foraging Optimization Algorithm and Particle Swarm Optimization (hBFOA-PSO) and the obtained results are compared with state of art optimization techniques. The results of the proposed model have been evaluated on a standard image dataset. The results obtained after implementing a 2-D histogram suggest hBFOA-PSO can be effectively used won multilevel thresholding problems resulting in a high accuracy.

A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification

A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification

Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems

In this paper, design and performance analysis of bacterial foraging optimization algorithm (BFOA)-optimized fuzzy PI/PID (FPI/FPID) controller for automatic generation control of multi-area interconnected traditional/restructured electrical power systems is presented. Firstly a traditional two-area non-reheat thermal system is considered, and gains of the fuzzy controller are tuned employing BFOA using integral of squared error objective function. The supremacy of this controller is demonstrated by juxtaposing the results with particle swarm optimization (PSO), firefly algorithm (FA), BFOA, hybrid BFOA–PSO-based PI and fuzzy PI controllers based upon pattern search (PS) and PSO algorithms for the same power system structure. The approach is then extended to a two-area reheat system, and improved results are found with the purported FPI/FPID controller in comparison with PSO and artificial bee colony optimized PI controller. Further, the approach is implemented on a traditional multi-source multi-area (MSMA) hydrothermal system and its superb performance is observed over genetic algorithm and hybrid FA–PS tuned PI controller. Additionally, to demonstrate the scalability of the designed controller to cope with restructured power system, the study is also protracted to a restructured MSMA hydrothermal power system. Finally, sensitivity analysis is performed to ascertain the robustness of the controller designed for the systems under study. It is observed that the suggested FPI/FPID controller optimized for nominal conditions is able to handle generation rate constraints and wide variations in nominal loading condition as well as system parameters.