Swarm Strategy Research Articles

An efficient hybrid particle swarm strategy, ray optimizer, and harmony search algorithm for optimal design of truss structures

In this paper a metaheuristic algorithm composed of particle swarm, ray optimization, and harmony search (HRPSO) is presented for optimal design of truss structures. This algorithm is based on the particle swarm ray origin making is used to update the positions of the particles, and for enhancing the exploitation of the algorithm the harmony search is utilized. Numerical results demonstrate the efficiency and robustness of the HRPSO method compared to some standard metaheuristic algorithms.

From organized internal traffic to collective navigation of bacterial swarms

Bacterial swarming resulting in collective navigation over surfaces provides a valuable example of cooperative colonization of new territories. The social bacterium Paenibacillus vortex exhibits successful and diverse swarming strategies. When grown on hard agar surfaces with peptone, P. vortex develops complex colonies of vortices (rotating bacterial aggregates). In contrast, during growth on Mueller–Hinton broth gelled into a soft agar surface, a new strategy of multi-level organization is revealed: the colonies are organized into a special network of swarms (or ‘snakes’ of a fraction of millimeter in width) with intricate internal traffic. More specifically, cell movement is organized in two or three lanes of bacteria traveling between the back and the front of the swarm. This special form of cellular logistics suggests new methods in which bacteria can share resources and risk while searching for food or migrating into new territories. While the vortices-based organization on hard agar surfaces has been modeled before, here, we introduce a new multi-agent bacterial swarming model devised to capture the swarms-based organization on soft surfaces. We test two putative generic mechanisms that may underlie the observed swarming logistics: (i) chemo-activated taxis in response to chemical cues and (ii) special align-and-push interactions between the bacteria and the boundary of the layer of lubricant collectively generated by the swarming bacteria. Using realistic parameters, the model captures the observed phenomena with semi-quantitative agreement in terms of the velocity as well as the dynamics of the swarm and its envelope. This agreement implies that the bacteria interactions with the swarm boundary play a crucial role in mediating the interplay between the collective movement of the swarm and the internal traffic dynamics.

Shape and size optimization of trusses with multiple frequency constraints using harmony search and ray optimizer for enhancing the particle swarm optimization algorithm

In this paper, size and shape optimization of truss structures is performed using an efficient hybrid method. This algorithm uses a particle swarm strategy and ray optimizer, and utilizes additional harmony search for a better exploitation. Here, multiple frequency constraints are considered making the optimization a highly nonlinear problem. Some basic benchmark problems are solved by this hybrid method, and the numerical results demonstrate the efficiency and robustness of this method compared to other mathematical and heuristic algorithms.

Multiband fractal Zigbee/WLAN antenna for ubiquitous wireless environments

In this paper, a multiband antenna based on a perturbed Sierpinski Gasket fractal for distributed systems in ubiquitous wireless environments is presented. The antenna enables communication services within the 868/915 MHz ZigBee bands and in the 2.4 GHz WLAN frequencies. The geometrical characteristics of the radiator are determined to fit the electrical and radiation requirements through an optimization approach based on a particle swarm strategy. The prototype performances are assessed through numerical and experimental validations.

Hybrid Design of a Fractal-Shaped GSM/UMTS Antenna

In this paper, the synthesis of a triple-band planar antenna working in the GSM (900 and 1800 MHz) and UMTS frequency bands is presented. As reference geometry, a hybrid pre-fractal shape has been adopted by integrating a Sierpinski and a Meander-line structure. The synthesis of the antenna has been performed by optimizing the descriptive geometrical parameters of the reference shape by means of a customized Particle Swarm strategy to comply with the electrical and geometrical requirements.

An approach based on particle swarm computation to study the nanoscale DG MOSFET-based circuits

The analytical modeling of nanoscale Double-Gate MOSFETs (DG) requires generally several necessary simplifying assumptions to lead to compact expressions of current-voltage characteristics for nanoscale CMOS circuits design. Further, progress in the development, design and optimization of nanoscale devices necessarily require new theory and modeling tools in order to improve the accuracy and the computational time of circuits' simulators. In this paper, we propose a new particle swarm strategy to study the nanoscale CMOS circuits. The latter is based on the 2-D numerical Non-Equilibrium Green's Function (NEGF) simulation and a new extended long channel DG MOSFET compact model. Good agreement between our results and numerical simulations has been found. The developed model can also be incorporated into the nano-CMOS circuits' simulators to study CMOS-based devices without impact on the computational time and data storage.

Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization

A multiple-swarm multiobjective particle swarm optimization (PSO) algorithm, named dynamic multiple swarms in multiobjective PSO, is proposed in which the number of swarms is adaptively adjusted throughout the search process via the proposed dynamic swarm strategy. The strategy allocates an appropriate number of swarms as required to support convergence and diversity criteria among the swarms. Additional novel designs include a PSO updating mechanism to better manage the communication within a swarm and among swarms and an objective space compression and expansion strategy to progressively exploit the objective space during the search process. Comparative study shows that the performance of the proposed algorithm is competitive in comparison to the selected algorithms on standard benchmark problems. In particular, when dealing with test problems with multiple local Pareto fronts, the proposed algorithm is much less computationally demanding. Sensitivity analysis indicates that the proposed algorithm is insensitive to most of the user-specified design parameters.

A Hybrid Prefractal Three-Band Antenna for Multistandard Mobile Wireless Applications

In this letter, the synthesis of a miniaturized three-band planar antenna working in GSM and WiFi frequency bands is described. The reference geometry for the synthesis of the antenna is a hybrid prefractal shape obtained by integrating a Sierpinski-like and a Meander-like structure. The synthesis of the geometrical parameters of the antenna has been performed by means of a customized particle swarm strategy to yield electrical parameters within given specifications. In order to show the effectiveness of the approach, some results from the numerical synthesis procedure are described and a comparison between simulations and experimental measurements is reported.

Evolved swarming without positioning information: an application in aerial communication relay

In most swarm systems, agents are either aware of the position of their direct neighbors or they possess a substrate on which they can deposit information (stigmergy). However, such resources are not always obtainable in real-world applications because of hardware and environmental constraints. In this paper we study in 2D simulation the design of a swarm system which does not make use of positioning information or stigmergy.This endeavor is motivated by an application whereby a large number of Swarming Micro Air Vehicles (SMAVs), of fixed-wing configuration, must organize autonomously to establish a wireless communication network (SMAVNET) between users located on ground. Rather than relative or absolute positioning, agents must rely only on their own heading measurements and local communication with neighbors.Designing local interactions responsible for the emergence of the SMAVNET deployment and maintenance is a challenging task. For this reason, artificial evolution is used to automatically develop neuronal controllers for the swarm of homogenous agents. This approach has the advantage of yielding original and efficient swarming strategies. A detailed behavioral analysis is then performed on the fittest swarm to gain insight as to the behavior of the individual agents.

A Multiagent Swarming System for Distributed Automatic Target Recognition Using Unmanned Aerial Vehicles

Over the past few years, automatic target recognition (ATR) has emerged as an essential image analysis tool to identify objects from temporally and spatially disjoint possibly noisy image data. For many current applications, ATR is performed by unmanned aerial vehicles (UAVs) that fly within a reconnaissance area to collect image data through sensors and upload the data to a central ground control station for analyzing and identifying potential targets. The centralized approach to ATR introduces several problems, including scalability with the number of UAVs, network delays in communicating with the central location, and the susceptibility of the system to malicious attacks on the central location. These challenges can be addressed by using a distributed system for performing ATR. In this paper, we describe a multiagent-based prototype system that uses swarming techniques inspired from insect colonies to perform ATR using UAVs in a distributed manner within simulated scenarios. We assume that UAVs are constrained in the resources available onboard and in their capabilities for performing ATR due to payload limitations. Our focus in this paper is on the coordination aspects between UAVs to efficiently decide how they are to act by using a swarming mechanism. We describe algorithms for the different operations performed by the UAVs in the system and for different swarming strategies, which are embedded within software agents located on the UAVs. We provide empirical simulations of our system within a simulated area of interest to determine its behavior in different scenarios with varying operational constraints. Our experimental results indicate that swarming strategies for distributed ATR perform favorably compared with centralized ATR strategies.

Synthesis of a Galileo and Wi-Max Three-Band Fractal-Eroded Patch Antenna

In this letter, the synthesis of a three-band patch antenna working in E5-L1 Galileo and Wi-Max frequency bands is described. The geometry of the antenna is defined by performing a Koch-like erosion in a classical rectangular patch structure according to a Particle Swarm strategy to optimize the values of the electrical parameters within given specifications. In order to assess the effectiveness of the antenna design, some results from the numerical synthesis procedure are described and a comparison between simulations and experimental measurements is reported.

Computational Swarm Strategies for Single Objective Design Optimization Problems

In this paper, three distinct swarm strategies for the optimization of engineering design problems with solitary objectives are presented. These strategies build upon the swarm algorithm of Ray et al. [1] by incorporating new processes that improve the performance of their predecessor. The Constraint Matching strategy calls for the mating of solutions based on constraint satisfaction characteristics. Local Search entails the thorough exploration of regions in parametric space where good solutions potentially reside. Migrating Leaders prescribes the exchange of information between the best performing members of the swarm. As proof of their utility, the strategies were used to solve two well-studied optimal engineering design problems. Solutions obtained by the strategies compare favorably against those of other optimization approaches documented in the literature.

An Innovative Computational Approach Based on a Particle Swarm Strategy for Adaptive Phased-Arrays Control

In this paper a new approach to the control of phased arrays is presented and assessed. Starting from the adaptive array theory, a particle swarm strategy is used to tune the phase coefficients of the array in order to adaptively minimize/avoid the effects of interfering signals at the receiver. To show the effectiveness of the proposed approach, a selected set of numerical examples, concerned with linear as well as planar arrays, is presented. Furthermore, to evaluate the advantages of the particle swarm optimizer (PSO)-based strategy over state-of-art methods, a comparative study is carried out by analyzing the performance of the method in terms of both the signal-to-interference-plus-noise-ratio and resulting beam pattern. The achieved results, even though preliminary, seem to confirm that the PSO-based approach satisfactorily works and it generally outperforms previously proposed/state-of-art phase-only adaptive control strategies

Leader identification and leader selection: its effect on a swarm?s performance for multi-objective design optimization problems

In this paper, three distinct swarm strategies for the optimization of engineering design problems with multiple objectives are presented. These strategies build upon the swarm algorithm of Ray et al. by incorporating new processes which improve the performance of their predecessor. The constraint-matching strategy calls for the mating of solutions based on constraint satisfaction characteristics. Local search entails the thorough exploration of regions in parametric space where good solutions potentially reside. Migrating leaders prescribes the exchange of information between the best performing members of the swarm. As proof of their utility, the strategies were used to solve two well-studied optimal engineering design problems. Solutions obtained by the strategies are comparable with those of other optimization approaches documented in the literature.

Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients

This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC). Under this method, only the social part and the cognitive part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks, mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the acceleration coefficients are fixed at two.

Swarming Strategies for Cooperative Species

In this paper, we propose a model to investigate the relative efficiency of simple swarming strategies based on the interplay between spontaneous and recruitment-based emigration. We conduct a dynamical study of the model which combines inverse density dependence, saturation effects and induced vs. diffusion-like population transfer. The influence of the most relevant parameters is explored on a systematic basis, and transition values for which qualitative changes occur in the system's behaviour are given. The model is then used to study colonization of a multiple sites environment, as well as confrontation between species featuring different swarming strategies. Simulation results indicate that cooperative organisms should have an interest in evolving recruitment-based emigration. The corresponding population transfer patterns prove more efficient in invading new territories, eliminating competitors in the process. We suggest that this advantage could have promoted a simple form of coordinated swarming in species featuring a primitive type of cooperation.

ENGINEERING DESIGN OPTIMIZATION USING A SWARM WITH AN INTELLIGENT INFORMATION SHARING AMONG INDIVIDUALS

In this paper a new swarm algorithm for single objective design optimization problems is presented. A swarm is a collection of individuals having a common goal to reach the best value (minimum or maximum) of a function. Among the individuals in a swarm, there are some better performers (leaders) who set the direction of search for the rest of the individuals. An individual that is not in the better performer list (BPL) improves its performance by deriving information from its closest neighbour in the BPL. In an unconstrained problem, the objective values are used to generate the BPL while a multilevel Pareto ranking scheme is implemented to generate the BPL for constrained problems. The information sharing strategy also ensures that all the individuals in the swarm are unique as in a real swarm, where at a given time instant two individuals cannot share the same location. The uniqueness among the individuals result in a set of near optimal individuals at the final stage that is useful for sensitivity analysis. Three well-studied engineering design examples are solved to illustrate the benefits of the proposed swarm strategy

Generation and Adaptation Mechanism of Swarm Strategy for Multi-Agent System. Optimization Algorithm of Swarm Strategy by Biologically Inspired Immune Type Swarm Robotic System.

This paper presents generation and adaptation mechanism of the swarm strategy for a multi-agent system in a dynamic environment. The approach for the adaptation of the swarm strategy is based on the clonal selection theory of the biological immune system. The immune system possesses a homeostasis maintenance mechanism which allows for recognition of self and non-self, and removal of foreign substances from the body. In this paper, we adopt some of the immune mechanisms for each agent and for interaction between agents. Tactics the mechanism we present here, is one method to search and adapt the optimal swarm strategy in a dynamic environment. By using this tactics cloning, individual tactics or a set of individual tactics generates the most optimal swarm strategy.