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Smart swarm inspired algorithms for microwave imaging problems

Smart swarm inspired algorithms for microwave imaging problems

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In the last decade smart swarm inspired optimization algorithms, such as particle swarm optimizer (PSO) [1], ant colony optimizer (ACO) [2], firefly optimizer (FFO) [3], bacteria foraging optimizer (BFO) [4], honey bees optimizer (HBO) [5] and others algorithms [6,7], have been successfully adopted as a powerful optimization tools in several areas of applied engineering, and they demonstrated their advantages and superiority with respect to optimizers based on natural competition such as genetic algorithms (GAs) [8], different evolution (DE) [9] and their customizations. The development of CAD tools based on smart swarm optimizer (SSO) could provide the researchers, engineers and industrial designers with powerful tools that can be the solution for the industrial market since they permit to reduce the time to market of a specific device keeping the commercial predominance. It is worth noticing that these tools do not require expert engineers and they can reduce the computational time typical of the standard trial errors methodologies. This chapter is aimed at presenting an overview of smart swarm inspired optimization algorithms (SSOs) as applied to the solution of complex engineering problems. The overview starts from the wellknown GAs up to recent collaborative optimizers based on intelligent swarms and inspired by nature. In particular, SSOs are mimic the behaviour of insects, birds, bats or flock of fishes, searching for food. The goal of this chapter is on the use, calibration and the capabilities assessment of different kind of smart swarms based optimization algorithms for the solution of complex engineering problems. In order to apply a smart swarm inspired algorithm an engineering problem is usually recast into a global optimization problem. Formulated in such a way, complex problems can be efficiently handled by smart swarm inspired optimizer by defining a suitable cost function that represent the distance between requirements and the trial solution. The effectiveness of a given trial solution can be analysed with numerical methodologies, such as finite element method (FEM), finite difference time domain (FDTD), method of moment (MoM) simulator, and then compared with the initial requirements. As a common feature, these environments usually integrate an optimizer and commercial numerical simulators. In particular, this chapter describes how to solve a set of electromagnetic problems, typically characterized by high unknown number, and strong nonlinearities. An example of complex electromagnetic problem is a typical microwave imaging application [10] or the control [11] and design [12] of complex radiating structures, the calibration of microwave systems and other interesting practical applications [13]. The first two section deal with an accurate description of competitive algorithms [such as GAs, differential evolution (DE) and their customization] versus collaborative algorithms [namely PSO [1], ACO [2], FFO [3], BFO [4], artificial honey bees (AHB) [5] and others]. In the first two sections, the key of force and the limitations of both competitive as well as collaborative will be reported and discussed. Theoretical discussions concerned convergence issues, parameters sensitivity analysis and computational burden estimation are reported as well. Successively, a brief review on how different research groups have applied or customized different nature inspired optimizations (NIOs) approaches for the solution of complex practical electromagnetic problems ranging from industrial up to biomedical applications. The chapter ends with open problems and discussion on future applications.

Chapter Contents:

  • Abstract
  • 14.1 Nature inspired algorithms
  • 14.1.1 Competitive algorithms
  • 14.1.2 Collaborative algorithms
  • 14.1.2.1 The particle swarm optimizer (PSO)
  • 14.1.2.2 The artificial honey bee optimizer
  • 14.1.2.3 The bacteria feeding optimizer
  • 14.1.2.4 The ant colony optimizer
  • 14.1.2.5 The bat optimizer
  • 14.1.2.6 The firefly optimizer
  • 14.2 Algorithms assessment
  • 14.2.1 Description of the considered benchmark problem
  • 14.3 Conclusions
  • References

Inspec keywords: particle swarm optimisation; finite element analysis; method of moments; calibration; finite difference time-domain analysis; microwave imaging; ant colony optimisation

Other keywords: SSO; calibration; finite element method; DE; standard trial errors methodologies; artificial honey bees optimizer; MoM simulator; finite difference time domain method; ACO; NIO; bacteria foraging optimizer; particle swarm optimizer; PSO; different evolution; biomedical applications; FFO; FEM; method of moment simulator; BFO; smart swarm inspired optimization algorithms; complex practical electromagnetic problems; CAD tools; AHB; microwave imaging application; FDTD method; complex engineering problems; intelligent swarms; genetic algorithms; firefly optimizer; nature inspired optimizations; ant colony optimizer; GA; HBO; numerical simulators

Subjects: Measurement standards and calibration; Microwave measurement techniques; Finite element analysis; Optimisation techniques

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