942 research outputs found
A novel hybrid bacteria-chemotaxis spiral-dynamic algorithm with application to modelling of flexible systems
This paper presents a novel hybrid optimisation algorithm namely HBCSD, which synergises a bacterial foraging algorithm (BFA) and spiral dynamics algorithm (SDA). The main objective of this strategy is to develop an algorithm that is capable to reach a global optimum point at the end of the final solution with a faster convergence speed compared to its predecessor algorithms. The BFA is incorporated into the algorithm to act as a global search or exploration phase. The solutions from the exploration phase then feed into SDA, which acts as a local search or exploitation phase. The proposed algorithm is used in dynamic modelling of two types of flexible systems, namely a flexible robot manipulator and a twin rotor system. The results obtained show that the proposed algorithm outperforms its predecessor algorithms in terms of fitness accuracy, convergence speed, and time-domain and frequency-domain dynamic characterisation of the two flexible systems. © 2014 Elsevier Ltd
Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines
This paper presents the implementation of the hybrid spiral-dynamic bacteria-chemotaxis (HSDBC) approach to control two different configurations of a two-wheeled vehicle. The HSDBC is a combination of bacterial chemotaxis used in bacterial forging algorithm (BFA) and the spiral-dynamic algorithm (SDA). BFA provides a good exploration strategy due to the chemotaxis approach. However, it endures an oscillation problem near the end of the search process when using a large step size. Conversely; for a small step size, it affords better exploitation and accuracy with slower convergence. SDA provides better stability when approaching an optimum point and has faster convergence speed. This may cause the search agents to get trapped into local optima which results in low accurate solution. HSDBC exploits the chemotactic strategy of BFA and fitness accuracy and convergence speed of SDA so as to overcome the problems associated with both the SDA and BFA algorithms alone. The HSDBC thus developed is evaluated in optimizing the performance and energy consumption of two highly nonlinear platforms, namely single and double inverted pendulum-like vehicles with an extended rod. Comparative results with BFA and SDA show that the proposed algorithm is able to result in better performance of the highly nonlinear systems
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A survey of swarm intelligence for dynamic optimization: algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given
Multiple robot co-ordination using particle swarm optimisation and bacteria foraging algorithm
The use of multiple robots to accomplish a task is certainly preferable over the use of specialised individual robots. A major problem with individual specialized robots is the idle-time, which can be reduced by the use of multiple general robots, therefore making the process economical. In case of infrequent tasks, unlike the ones like assembly line, the use of dedicated robots is not cost-effective. In such cases, multiple robots become essential. This work involves path-planning and co-ordination between multiple mobile agents in a static-obstacle environment. Multiple small robots (swarms) can work together to accomplish the designated tasks that are difficult or impossible for a single robot to accomplish. Here Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) have been used for coordination and path-planning of the robots. PSO is used for global path planning of all the robotic agents in the workspace. The calculated paths of the robots are further optimized using a localised BFA optimization technique. The problem considered in this project is coordination of multiple mobile agents in a predefined environment using multiple small mobile robots. This work demonstrates the use of a combinatorial PSO algorithm with a novel local search enhanced by the use of BFA to help in efficient path planning limiting the chances of PSO getting trapped in the local optima. The approach has been simulated on a graphical interface
Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator
A novel hybrid strategy combining a spiral dynamic algorithm (SDA) and a bacterial foraging algorithm (BFA) is presented in this article. A spiral model is incorporated into the chemotaxis of the BFA algorithm to enhance the capability of exploration and exploitation phases of both SDA and BFA with the aim to improve the fitness accuracy for the SDA and the convergence speed as well as the fitness accuracy for BFA. The proposed algorithm is tested with the Congress on Evolutionary Computation 2013 (CEC2013) benchmark functions, and its performance in terms of accuracy is compared with its predecessor algorithms. Consequently, for solving a complex engineering problem, the proposed algorithm is employed to obtain and optimise the fuzzy logic control parameters for the hub angle tracking of a flexible manipulator system. Analysis of the performance test with the benchmark functions shows that the proposed algorithm outperforms its predecessor algorithms with significant improvements and has a competitive performance compared to other well-known algorithms. In the context of solving a real-world problem, it is shown that the proposed algorithm achieves a faster convergence speed and a more accurate solution. Moreover, the time-domain response of the hub angle shows that the controller optimised by the proposed algorithm tracks the desired system response very well
Optimisation algorithms inspired from modelling of bacterial foraging patterns and their applications
Research in biologically-inspired optimisation has been fl<;lurishing over the past decades. This approach adopts a bott0!ll-up viewpoint to understand and mimic certain features of a biological system. It has been proved useful in developing nondeterministic algorithms, such as Evolutionary Algorithms (EAs) and Swarm Intelligence (SI). Bacteria, as the simplest creature in nature, are of particular interest in recent studies. In the past thousands of millions of years, bacteria have exhibited a self-organising behaviour to cope with the natural selection. For example, bacteria have developed a number of strategies to search for food sources with a very efficient manner. This thesis explores the potential of understanding of a biological system by modelling the' underlying mechanisms of bacterial foraging patterns and investigates their applicability to engineering optimisation problems. :rvlodelling plays a significant role in understanding bacterial foraging behaviour. Mathematical expressions and experimental observations have been utilised to represent biological systems. However, difficulties arise from the lack of systematic analysis of the developed models and experimental data. Recently, Systems Biology has be,en proposed to overcome this barrier, with the effort from a number of research fields, including Computer Science and Systems Engineering. At the same time, Individual-based Modelling (IbM) has emerged to assist the modelling of a biological system. Starting from a basic model of foraging and proliferation of bacteria, the development of an IbM of bacterial systems of this thesis focuses on a Varying Environment BActerial Model (VEBAM). Simulation results demonstrate that VEBAM is able to provide a new perspective to describe interactions between the bacteria and their food environment. Knowledge transfer from modelling of bacterial systems to solving optimisation problems also composes an important part of this study. Three Bacteriainspired Algorithms (BalAs) have been developed to bridge the gap between modelling and optimisation. These algorithms make use of the. self-adaptability of individual bacteria in the group searching activities described in VEBAM, while incorporating a variety of additional features. In particular, the new bacterial foraging algorithm with varying population (BFAVP) takes bacterial metabolism into consideration. The group behaviour in Particle Swarm Optimiser (PSO) is adopted in Bacterial Swarming Algorithm (BSA) to enhance searching ability. To reduce computational time, another algorithm, a Paired-bacteria Optimiser (PBO) is designed specifically to further explore the capability of BalAs. Simulation studies undertaken against a wide range of benchmark functions demonstrate a satisfying performance with a reasonable convergence speed. To explore the potential of bacterial searching ability in optimisation undertaken in a varying environment, a dynamic bacterial foraging algorithm (DBFA) is developed with the aim of solving optimisation in a time-varying environment. In this case, the balance between its convergence and exploration abilities is investigated, and a new scheme of reproduction is developed which is different froin that used for static optimisation problems. The simulation studies have been undertaken and the results show that the DBFA can adapt to various environmental changes rapidly. One of the challenging large-scale complex optimisation problems is optimal power flow (OPF) computation. BFAVP shows its advantage in solving this problem. A simulation study has been performed on an IEEE 30-bus system, and the results are compared with PSO algorithm and Fast Evolutionary Programming (FEP) algorithm, respectively. Furthermore, the OPF problem is extended for consideration in varying environments, on which DBFA has been evaluated. A simulation study has been undertaken on both the IEEE 30-bus system and the IEEE l1S-bus system, in compariso~ with a number of existing algorithms. The dynamic OPF problem has been tackled for the first time in the area of power systems, and the results obtained are encouraging, with a significant amount of energy could possibly being saved. Another application of BaIA in this thesis is concerned with estimating optimal parameters of a power transformer winding model using BSA. Compared with Genetic Algorithm (GA), BSA is able to obtain a more satisfying result in modelling the transformer winding, which could not be achieved using a theoretical transfer function model
Past, present and future of path-planning algorithms for mobile robot navigation in dynamic environments
Mobile robots have been making a significant contribution to the advancement of many sectors including automation of mining, space, surveillance, military, health, agriculture and many more. Safe and efficient navigation is a fundamental requirement of mobile robots, thus, the demand for advanced algorithms rapidly increased. Mobile robot navigation encompasses the following four requirements: perception, localization, path-planning and motion control. Among those, path-planning is a vital part of a fast, secure operation. During the last couple of decades, many path-planning algorithms were developed. Despite most of the mobile robot applications being in dynamic environments, the number of algorithms capable of navigating robots in dynamic environments is limited. This paper presents a qualitative comparative study of the up-to-date mobile robot path-planning methods capable of navigating robots in dynamic environments. The paper discusses both classical and heuristic methods including artificial potential field, genetic algorithm, fuzzy logic, neural networks, artificial bee colony, particle swarm optimization, bacterial foraging optimization, ant-colony and Agoraphilic algorithm. The general advantages and disadvantages of each method are discussed. Furthermore, the commonly used state-of-the-art methods are critically analyzed based on six performance criteria: algorithm's ability to navigate in dynamically cluttered areas, moving goal hunting ability, object tracking ability, object path prediction ability, incorporating the obstacle velocity in the decision, validation by simulation and experimentation. This investigation benefits researchers in choosing suitable path-planning methods for different applications as well as identifying gaps in this field. © 2020 IEEE
An Improved Adaptive Spiral Dynamic Algorithm for Global Optimization
This paper proposes a new strategy to enhance the performance and accuracy of the Spiral dynamic algorithm (SDA) for use in solving real-world problems by hybridizing the SDA with the Bacterial Foraging optimization algorithm (BFA). The dynamic step size of SDA makes it a useful exploitation approach. However, it has limited exploration throughout the diversification phase, which results in getting trapped at local optima. The optimal initialization position for the SDA algorithm has been determined with the help of the chemotactic strategy of the BFA optimization algorithm, which has been utilized to improve the exploration approach of the SDA. The proposed Hybrid Adaptive Spiral Dynamic Bacterial Foraging (HASDBF) algorithm is designed so that the chemotaxis phase of bacteria represents the exploration part of the search operation. In contrast, the SDA represents the exploitation part.
Additionally, to improve search operation efficiency, the spiral model's radius and angular displacement are adaptively set according to a linear correlation concerning the fitness value. An additional phase, the elimination and dispersal phase, is obtained from BFA and added to the end of the SDA. This phase aims to improve the algorithm's final solution's accuracy by enhancing the algorithm's search strategy and performance. Simulation tests are run on unimodal and multimodal standard benchmark functions to verify the proposed algorithm. The proposed algorithm significantly outperforms SDA and Adaptive SDA (ASDA) algorithms regarding fitness value and accuracy.
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