Bacterial Chemotaxis Algorithm Research Articles

Modelling the Selection of Waiting Areas on Subway Platforms Based on the Bacterial Chemotaxis Algorithm

Modelling the Selection of Waiting Areas on Subway Platforms Based on the Bacterial Chemotaxis Algorithm

Research on optimisation of integrated energy system scheduling based on weak robust optimisation theory

This study establishes a multi-objective model for comprehensive energy system scheduling optimisation with wind farms, combined heat and power units, power to gas technology, circulating fluidised bed boilers and gas boilers. The goal is to minimise the operating costs and minimise the discharge of pollutants while meeting the constraints of the material balance of the circulating fluidised bed boiler. The improved weak robust optimisation theory is used to deal with the uncertainty of wind power, and the weak robust coefficient is introduced. By adjusting the coefficient, the optimal solution under the different tolerances of the deterioration of the objective function is obtained. The improved bacterial population chemotaxis algorithm is used to optimise the comprehensive energy system scheduling model, and a feasible solution with certain robustness is obtained. Sensitivity analysis under different risk levels is done. The simulation verifies the correctness and effectiveness of the optimised scheduling model and the improved algorithm.

A New Medical Image Edge Detection Algorithm Based on BC-ACO

Taking the improved ant colony algorithm based on bacterial chemotaxis as a means, this paper proposes one new swarm intelligence optimization algorithm to solve the medical image edge detection problem. The improved ant colony algorithm based on bacterial chemotaxis mainly aims at the shortcoming that the basic ant colony algorithm lacks initial pheromone, and combines bacterial chemotaxis algorithm with basic ant colony algorithm. Firstly, feasible better solution can be found through bacterial chemotaxis algorithm and fed back as initial pheromone. Then ant colony algorithm is implemented to search for the global optimal solution. The algorithm test indicates that the improved ant colony algorithm is more effective in the aspects of searching precision, reliability, optimization speed and stability compared with basic ant colony algorithm. Finally, the improved ant colony algorithm is applied into the edge detection of medical image. It can be seen from the computer simulation that compared with other operators and basic ant colony algorithm on the issue of solving medical image edge detection, the improved ant colony algorithm has superiority and the detected edge is clearer.

Controlling Voltages and Reduction of Real Power Loss in Power System by Using Crossbreed Spiral Dynamics Bacterial Chemotaxis Algorithm

Controlling Voltages and Reduction of Real Power Loss in Power System by Using Crossbreed Spiral Dynamics Bacterial Chemotaxis Algorithm

Coordination Optimization of PSS Parameters based on Polymorphic Bacterial Chemotaxis Algorithm

In terms of damping low frequency oscillation, power system stabilizer(PSS) plays a very key role. However, in terms of PSS capability exploitation, it is quite important to select the suitable parameters assignment. Different from traditional optimization algorithms with eigenvalue analysis based and with system damping ratio as the aim function, in this paper a new PSS parameters method based on the Polymorphic Bacterial Chemotaxis(PBC) algorithm is presented for PSS parameters optimization. According to the time multiplied absolute error integral criterion (ITAE criteria), this paper chooses the ability of tracking a given value with minimum error of system output as the objective function. Thus the whole process of the disturbed system is considered. Finally, Eigenvalue analysis and nonlinear time-domain simulation are conducted to verify the feasibility and effectiveness of the proposed method.

Optimal Reactive Power Dispatch Based on Mixed Bacterial Chemotaxis Algorithm

Particle Swarm Optimization (PSO) algorithm converges fast but it is easy to fall into local optimum, and bacterial chemotaxis (BC) algorithm prevents premature convergence and prevents falling into local optimum, so a new mixed bacterial chemotaxis (MBC) algorithm is proposed by combining the PSO with BC. The novel algorithm is applied to reactive power optimization on power system. First the PSO is used to find best solution, then BC is used to find the optimal solution among the selected area of previous step, the reserving elite strategy is introduced to enhance the efficiency of the algorithm, and then the optimal solution is obtained. Through the comparison with PSO and BCC in the reactive power optimization of IEEE30-bus system, the results indicate that MBC not only prevents premature convergence to a large extent, but also keeps a more rapid convergence rate than PSO and BCC.

Bouc–Wen Hysteresis Model Identification by the Metric-Topological Evolutionary Optimization

From a computational point of view, the identification of Bouc-Wen (BW) hysteresis model is a hard task due to the large number of parameters to be found. This is one of the reasons for which it is rarely used for modeling magnetic hysteresis, where other hysteresis models are widely used (e.g., Preisach and Jiles-Atherton). However, the opportunities that the differential expression of BW model could offer for its use in more complex computation task (e.g., nonlinear inductors inserted into a circuit and so on) justify a deeper investigation on its adoption in ferromagnetism. In this paper, using a new hybrid heuristic called metric-topological-evolutionary optimization (MeTEO), the BW identification is presented. MeTEO is a powerful algorithm based on a synergic and strategic use of three evolutionary heuristics: 1) the flock-of-starlings optimization, which shows not only high exploration capability, but also a lack of convergence; 2) the particle swarm optimization, which has a good convergence capability; and 3) the bacterial chemotaxis algorithm, which has no collective behavior or exploration skill, but has high convergence capability. MeTEO is designed to use parallel architectures and exploits the fitness modification technique. Numerical validations are presented in comparison with the performances obtained using other approaches available in the literature.

Optimization of multistage depressed collectors using fem and parallel algorithm MeTEO

Purpose – This paper aims the application of a novel hybrid algorithm, called MeTEO, based on the combination of three heuristics inspired by artificial life to the optimization of electrodes voltages of multistage depressed collector. Design/methodology/approach – The flock-of-starlings optimization (FSO), the particle swarm optimization (PSO) and the bacterial chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO+BCA has been used to refine the FSO-found solutions, exploiting their better performances in local search. Findings – The optimization of the voltage of the electrodes of multistage depressed collector are efficiently handled with a moderate computational effort. Practical implication – The development of an efficient method for the solution of a complicated electromagnetic optimization problem, exploiting the different characteristic of different approaches based on evolutionary computation algorithm. Originality/value – The paper shows that the combination of stochastic methods having different exploration properties with appositely developed FE electromagnetic simulator allows us to produce effective solutions of multimodal electromagnetic optimization problems, with an acceptable computational cost.

Comparative analysis of Bouc–Wen and Jiles–Atherton models under symmetric excitations

The aim of the present paper is to validate the Bouc–Wen (BW) hysteresis model when it is applied to predict dynamic ferromagnetic loops. Indeed, although the Bouc–Wen model has had an increasing interest in last few years, it is usually adopted in mechanical and structural systems and very rarely for magnetic applications. Thus, for addressing this goal the Bouc–Wen model is compared with the dynamic Jiles–Atherton model that, instead, was ideated exactly for simulating magnetic hysteresis. The comparative analysis has involved saturated and symmetric hysteresis loops in ferromagnetic materials. In addition in order to identify the Bouc–Wen parameters a very effective recent heuristic, called Metric-Topological and Evolutionary Optimization (MeTEO) has been utilized. It is based on a hybridization of three meta-heuristics: the Flock-of-Starlings Optimization, the Particle Swarm Optimization and the Bacterial Chemotaxis Algorithm. Thanks to the specific properties of these heuristic, MeTEO allow us to achieve effective identification of such kind of models. Several hysteresis loops have been utilized for final validation tests with the aim to investigate if the BW model can follow the different hysteresis behaviors of both static (quasi-static) and dynamic cases.

Bacterial chemotaxis shape optimization of electromagnetic devices

In this paper, a bacterial chemotaxis shape optimization algorithm is presented for the optimal design of electromagnetic devices. The bacterial chemotaxis algorithm exploits the conceptual analogy between bacteria and mesh nodes, improving the devices performance by moving the specific mesh nodes/bacteria defining the shape in the same way the bacteria move toward regions presenting better life conditions. Validation tests are also presented in order to show the performance of the developed shape optimizer.

TWT magnetic focusing structure optimization by parallel evolutionary algorithm

PurposeThe purpose of this paper is to present the application of a novel hybrid algorithm, called MeTEO (Metric‐Topological‐Evolutionary‐Optimization), based on the combination of three heuristics inspired by artificial life to the solution of optimization problems of a real electronic vacuum device.Design/methodology/approachThe Particle Swarm Optimization (PSO), the Flock‐of‐Starlings Optimization (FSO) and the Bacterial Chemotaxis Algorithm (BCA) were adapted to implement a novel meta‐heuristic MeTEO the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO is used to explore local regions where FSO had found solutions, and BCA to refine the solutions found by PSO, thanks its better performances in local search.FindingsThe optimization of the focusing magnetic field of a Travelling Wave Tubes (TWT) collector is presented in order to show the effectiveness of MeTEO, in combination with COLLGUN FE simulator and equivalent source representation. The optimization of the focusing magnetic structure is obtained by using a maximum of 100 steps for each heuristic.Practical implicationsThe paper describes the development of a novel efficient parallel method for the solution of electromagnetic device optimization problems.Originality/valueThe paper shows the capabilities of a novel combination of optimization methods inspired by “artificial life” which allows us to achieve effective solutions of multimodal optimization problems, typical of the electromagnetic device optimization, with an acceptable computational cost, thanks also to its natural parallel implementation.

TEAM problem 22 approached by a hybrid artificial life method

PurposeThe purpose of this paper is to apply a hybrid algorithm based on the combination of two heuristics inspired by artificial life to the solution of optimization problems.Design/methodology/approachThe flock‐of‐starlings optimization (FSO) and the bacterial chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the BCA has been used to refine the FSO‐found solutions, thanks to its better performances in local search.FindingsA good solution of the 8‐th parameters version of the TEAM problem 22 is obtained by using a maximum 200 FSO steps combined with 20 BCA steps. Tests on an analytical function are presented in order to compare FSO, PSO and FSO+BCA algorithms.Practical implicationsThe development of an efficient method for the solution of optimization problems, exploiting the different characteristic of the two heuristic approaches.Originality/valueThe paper shows the combination and the interaction of stochastic methods having different exploration properties, which allows new algorithms able to produce effective solutions of multimodal optimization problems, with an acceptable computational cost, to be defined.

Shape Optimization of Multistage Depressed Collectors by Parallel Evolutionary Algorithm

In this paper a novel parallel meta-heuristic algorithm called MeTEO is presented, applied to the shape optimization of multistage depressed collectors, simulated by means of a Finite Element collector and electron gun simulator, COLLGUN, which uses the Constructive Solid Geometry for the description of the device shape. METEO is a hybrid algorithm composed by three different heuristics: FSO (Flock of Starlings Optimization), PSO (Particle Swarm Optimization), and BCA (Bacterial Chemotaxis Algorithm); it performs the optimization using both the topological and the metric rules and offers a natural parallel implementation that allows speeding up the whole process of optimization by the fitness modification (FM).

Metric-topological–evolutionary optimization

This article shows a novel approach for optimization and inverse problems based on evolutionary computation with the aim to satisfy two opposite requirements: exploration and convergence. The proposed approach is particularly suitable for parallel computing and it gives its best both for multimodal problems and for problems in which bad initializations can occur. The proposed algorithm has been called MeTEO to point out its metric-topological and evolutionary inspiration. In fact, it is based on a hybridization of two heuristics coming from swarm intelligence: the flock-of-starlings optimization (FSO; which shows high exploration capability but a lack of convergence), the standard particle swarm optimization (which is less explorative than FSO but with a good convergence capability) and a third evolutionary heuristic: the bacterial chemotaxis algorithm (that has no collective behaviour, no exploration skill but high convergence capability). Finally, with the aim of speeding up the algorithm, a technique that we call fitness modification is proposed and implemented. Suitable tests regarding optimization benchmarks and inverse problems will be presented with the aim of pointing out the MeTEO performances compared with those of each single heuristic used for hybridization.

Quality improvement of 3‐D FE mesh by a BCA approach

Purpose – The aim of this work is to show how evolutionary computation can improve the quality of 3D‐FE mesh that is a crucial task for field evaluations using 3‐D FEM analysis.Design/methodology/approach – The evolutionary approach used for optimizing 3D mesh generation is based on the bacterial chemotaxis algorithm (BCA). The objective function corresponds to the virtual bacterium best habitat, and the motion rules followed by each virtual bacterium are inspired to the natural behaviour of bacteria in real habitat.Findings – The obtained results show that the present approach returns good accuracy performances with low‐computational costs.Practical implications – The procedure is robust and converges for all the practical cases examined for validation.Originality/value – The adoption of a correct optimization algorithm is fundamental to obtain good performances in terms of robustness of the results and the low‐computational costs. In this sense, the BCA is a valid instrument for improving the quality of...