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The purpose of this paper is to describe a multi‐robot solution to the problem of chemical source localization, in which a team of inexpensive, simple vehicles with short‐range, low‐power sensing, communication, and processing capabilities trace a chemical plume to its source emitter

The source localization problem is analyzed using computational fluid dynamics simulations of airborne chemical plumes. The analysis is divided into two parts consisting of two large experiments each: the first part focuses on the issues of collaborative control, and the second part demonstrates how task performance is affected by the number of collaborating robots. Each experiment tests a key aspect of the problem, e.g. effects of obstacles, and defines performance metrics that help capture important characteristics of each solution.

The new empirical simulations confirmed previous theoretical predictions: a physics‐based approach is more effective than the biologically inspired methods in meeting the objectives of the plume‐tracing mission. This gain in performance is consistent across a variety of plume and environmental conditions. This work shows that high success rate can be achieved by robots using strictly local information and a fully decentralized, fault‐tolerant, and reactive control algorithm.

This is the first paper to compare a physics‐based approach against the leading alternatives for chemical plume tracing under a wide variety of fluid conditions and performance metrics. This is also the first presentation of the algorithms showing the specific mechanisms employed to achieve superior performance, including the underlying fluid and other physics principles and their numerical implementation, and the mechanisms that allow the practitioner to duplicate the outstanding performance of this approach under conditions of many robots navigating through obstacle‐dense environments.

The 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.

The 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.

A 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.

The development of an efficient method for the solution of optimization problems, exploiting the different characteristic of the two heuristic approaches.

The 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.

In light of the current international concerns with security and terrorism, interest is increasing on the topic of using robot swarms to locate the source of chemical hazards. The purpose of this paper is to place this task, called chemical plume tracing (CPT), in the context of fluid dynamics.

This paper provides a foundation for CPT based on the physics of fluid dynamics. The theoretical approach is founded upon source localization using the divergence theorem of vector calculus, and the fundamental underlying notion of the divergence of the chemical mass flux. A CPT algorithm called fluxotaxis is presented that follows the gradient of this mass flux to locate a chemical source emitter.

Theoretical results are presented confirming that fluxotaxis will guide a robot swarm toward chemical sources, and away from misleading chemical sinks. Complementary empirical results demonstrate that in simulation, a swarm of fluxotaxis‐guided mobile robots rapidly converges on a source emitter despite obstacles, realistic vehicle constraints, and flow regimes ranging from laminar to turbulent. Fluxotaxis outperforms the two leading competitors, and the theoretical results are confirmed experimentally. Furthermore, initial experiments on real robots show promise for CPT in relatively uncontrolled indoor environments.

A physics‐based approach is shown to be a viable alternative to existing mainly biomimetic approaches to CPT. It has the advantage of being analyzable using standard physics analysis methods.

The fluxotaxis algorithm for CPT is shown to be “correct” in the sense that it is guaranteed to point toward a true source emitter and not be fooled by fluid sinks. It is experimentally (in simulation), and in one case also theoretically, shown to be superior to its leading competitors at finding a source emitter in a wide variety of challenging realistic environments.

The purpose of the present paper is to show a comparative analysis of classical and modern heuristics such as genetic algorithms, simulated annealing, particle swarm optimization and bacterial chemotaxis, when they are applied to electrical engineering problems.

Hybrid algorithms (HAs) obtained by a synergy between the previous listed heuristics, with the eventual addiction of the Tabu Search, have also been compared with the single heuristic performances.

Empirically, a different sensitivity for initial values has been observed by changing type of heuristics. The comparative analysis has then been performed for two kind of problems depending on the dimension of the solution space to be inspected. All the proposed comparative analyses are referred to two corresponding different cases: Preisach hysteresis model identification (high dimension solution space) and load‐flow optimization in power systems (low dimension solution space).

The originality of the paper is to verify the performances of classical, modern and hybrid heuristics for electrical engineering applications by varying the heuristic typology and by varying the typology of the optimization problem. An original procedure to design a HA is also presented.

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.

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.

The optimization of the voltage of the electrodes of multistage depressed collector are efficiently handled with a moderate computational effort.

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.

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.

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.

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.

The obtained results show that the present approach returns good accuracy performances with low‐computational costs.

The procedure is robust and converges for all the practical cases examined for validation.

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 3D‐FE mesh.

The 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.

The 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.

The 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.

The paper describes the development of a novel efficient parallel method for the solution of electromagnetic device optimization problems.

The 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.

Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.

In view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.

The results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.

It is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.

Different representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

The purpose of this study is to guarantee assembly quality and reduce the number of manufacturing cycles required for an reflector of the large reflector antenna. An optimal approach combining a finite element method (FEM) with a genetic algorithm (GA) is developed to simulate and optimize reflector assembly before the assembly stage.

The chromosomes of GA are encoded with the consideration of the factors that affect the assembly of reflector. The fitness function of the GA consists of the assembly accuracy obtained from simulation, with evaluation of the assembly time consumption and labor cost. The algorithm will terminate when the GA is finished or the simulation results meet the permissible accuracy. Taking the assembly process of the reflector into account, an FEM based on a “life – death element” technique is introduced to quickly and precisely simulate reflector assembly.

A case study is presented, to which the proposed approach is applied. The results of finite element simulation demonstrate that the proposed FEM can simulate the reflector assembly process with oversimplified modeling and accurate simulation results. The optimal approach provides an accurate and efficient method for reflector assembly sequence planning indicated by the comparison of the measurements and calculation results.

The results also demonstrate that the proposed approach has practical significance for guiding reflector assembly in engineering practice.

As arc suppression coils (ASCs), magnetically controlled reactors (MCRs) are usually operated in the single-phase mode. Due to the lack of a third order harmonic compensation circuit, the current harmonics are high. The purpose of this paper is to propose a novel structure of MCR and a genetic algorithm (GA) to determine the parameters which will result in minimum total harmonics.

This paper proposes the structure and the working principle of the multi-valve controlled saturable reactor (MCSR). There are several sorts of magnetic valves in the iron cores of the MCSR. The saturation degree of each magnetic valve is different when the same direct component of the magnetic flux is generated in the iron core, therefore current harmonics of different phases emerging, i.e. the total harmonics can be reduced. The magnetization characteristics and the mathematical model of the current harmonics of the MCSR are presented by introducing three parameters. The optimal values of the parameters that result in the smallest total harmonic distortion in the output current are calculated by a GA.

The simulation and experimental results are coincident with the theoretical analyses, which prove the effectiveness of the proposed method on harmonic suppression.

The method proposed in this paper can successfully reduce the current harmonics of the conventional MCR, including but not limited to the ASC. A prototype MCSR (540 kVA/10 kV) has been designed and constructed.

In this paper, a MCSR is proposed. The mathematical model of the MCSR for harmonic analysis is developed. The optimal parameters that result in the smallest THD in the output current are calculated. The mathematical model can be also used for the harmonic analysis of conventional MCRs.