Sotirios K. Goudos

Application of a Comprehensive Learning Particle Swarm Optimizer to Unequally Spaced Linear Array Synthesis With Sidelobe Level Suppression and Null Control

We present unequally spaced linear array synthesis with sidelobe suppression under constraints to beamwidth and null control using a design technique based on a Comprehensive Learning Particle Swarm Optimizer (CLPSO). CLPSO utilizes a new learning strategy that achieves the goal to accelerate the convergence of the classical PSO. Numerical examples are compared to the existing array designs in the literature and to those found by the other evolutionary algorithms. The synthesis examples that are presented show that the CLPSO algorithm outperforms the common PSO algorithms and a real-coded genetic algorithm (GA).

Application of Taguchi's Optimization Method and Self-Adaptive Differential Evolution to the Synthesis of Linear Antenna Arrays

In this paper, the problem of designing linear antenna arrays for speciflc radiation properties is dealt with. The design problem is modeled as a single optimization problem. The objectives of this work are to minimize the maximum side lobe level (SLL) and perform null steering for isotropic linear antenna arrays by controlling difierent parameters of the array elements (position, amplitude, and phase). The optimization is performed using two techniques: Taguchis optimization method and the self-adaptive difierential evolution (SADE) technique. The advantage of Taguchis optimization technique is the ability of solving problems with a high degree of complexity using a small number of experiments in the optimization process Taguchis method is easy to implement and converges to the desired goal quickly in comparison with gradient-based methods and particle swarm optimization (PSO) Results obtained using Taguchis method are in very good agreement with those obtained using the SADE technique.

WSMO-PA: Formal Specification of Public Administration Service Model on Semantic Web Service Ontology

In this paper we define a formal model for a public administration service on the basis of the Web service modeling ontology (WSMO). For this purpose we employ the generic public service object model of the governance enterprise architecture (GEA) providing a PA domain specific semantics. We investigate conceptual mappings between PA entities and WSMO elements, and on the real-world use case present the detailed formal PA service model based on the WSMO service model

Model-driven eGovernment interoperability: A review of the state of the art

This paper reviews the state of the art in the area of enhancing eGovernment interoperability by using common models and/or ontologies. This area has currently become a very active research field. We identify and present a significant number (>40) of relevant efforts. These initiatives are grouped into categories based on the owner, scope and modelling perspective of each project. We then focus on the cases that build generic and universal eGovernment representations and models. We analyse, evaluate and rate them using an additional set of criteria. We end up with conclusions and possible directions for the exploitation and usage of these models.

Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution

Microwave filters play an important role in modern wireless communications. A novel method for the design of multilayer dielectric and open loop ring resonator (OLRR) filters under constraints is presented. The proposed design method is based on generalized differential evolution (GDE3), which is a multiobjective extension of differential evolution (DE). GDE3 algorithm can be applied for global optimization to any engineering problem with an arbitrary number of objective and constraint functions. GDE3 is compared against other evolutionary multiobjective algorithms like nondominated sorting genetic algorithm-II (NSGA-II), multiobjective particle swarm optimization (MOPSO) and multiobjective particle swarm optimization with fitness sharing (MOPSO-fs) for a number of microwave filter design cases. In the multilayer dielectric filter design case a predefined database of low loss dielectric materials is used. The results indicate the advantages of this approach and the applicability of this design method.

Sparse Linear Array Synthesis With Multiple Constraints Using Differential Evolution With Strategy Adaptation

This letter addresses the problem of designing sparse linear arrays with multiple constraints. The constraints could include the minimum and maximum distance between two adjacent elements, the total array length, the sidelobe level suppression in specified angular intervals, the main-lobe beamwidth, and the predefined number of elements. Our design method is based on differential evolution (DE) with strategy adaptation. We apply a DE algorithm (SaDE) that uses previous experience in both trial vector generation strategies and control parameter tuning. Design cases found in the literature are compared to those found by SaDE and other DE algorithms. The results show that fewer objective-function evaluations are required than those reported in the literature to obtain better designs. SaDE also outperforms the other DE algorithms in terms of statistical results.

Thinned Planar Array Design Using Boolean PSO With Velocity Mutation

The design of thinned planar microstrip arrays under specific constraints concerning the impedance-matching condition of the array elements and the radiation pattern is presented. The radiation characteristics of the structure are extracted by applying the method-of-moments. The array design is based on a novel optimization method, which is a modified version of the boolean particle swarm optimization that employs velocity mutation (BPSO-vm). Apart from the optimization of the array geometry, the proposed method is applicable to other discrete-variable optimization problems. Moreover, the planar array design is coped with by means of other techniques, namely, a binary coded Genetic Algorithm, the binary Particle Swarm Optimization, and the Boolean PSO. The comparison of the above methods and the BPSO-vm shows the efficiency of the proposed technique.

A Multi-Objective Approach to Subarrayed Linear Antenna Arrays Design Based on Memetic Differential Evolution

In this paper we present a multi-objective optimization approach to subarrayed linear antenna arrays design. We define this problem as a bi-objective one. We consider two objective functions for directivity maximization and sidelobe level minimization. Memetic algorithms (MAs) are hybrid algorithms that combine the benefits of a global search Evolutionary Algorithm (EA) with a local search method. In this paper, we introduce a new memetic multi-objective evolutionary algorithm namely the memetic generalized differential evolution (MGDE3). This algorithm is a memetic extension of the popular generalized differential evolution (GDE3) algorithm. Another popular MOEA is the nondominated sorting genetic algorithm-II (NSGA-II). MGDE3, GDE3 and NSGA-II are applied to the synthesis of uniform and nonuniform subarrayed linear arrays, providing an extensive set of solutions for each design case. Depending on the desired array characteristics, the designer can select the most suitable solution. The results of the proposed method are compared with those reported in the literature, indicating the advantages and applicability of the multi-objective approach.

Pareto Optimal Yagi-Uda Antenna Design Using Multi-Objective Differential Evolution

Antenna design problems often require the optimization of several con∞icting objectives such as gain maximization, sidelobe level (SLL) reduction and input impedance matching. Multi- objective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. An e-cient algorithm is Generalized Difierential Evolution (GDE3), which is a multi-objective extension of Difierential Evolution (DE). The GDE3 algorithm can be applied to global optimization of any engineering problem with an arbitrary number of objective and constraint functions. Another popular MOEA is Nondominated Sorting Genetic Algorithm- II (NSGA-II). Both GDE3 and NSGA-II are applied to Yagi-Uda antenna design under specifled constraints. The numerical solver used for antenna parameters calculations is SuperNEC, an object-oriented version of the numerical electromagnetic code (NEC-2). Three difierent Yagi-Uda antenna designs are considered and optimized. Pareto fronts are produced for both algorithms. The results indicate the advantages of this approach and the applicability of this design method.

Application of a Differential Evolution Algorithm with Strategy Adaptation to the Design of Multi-Band Microwave Filters for Wireless Communications

In this paper, we present a new method for the design of multi-band microstrip fllters. The proposed design method is based on Difierential Evolution (DE) with strategy adaptation. This self- adaptive DE (SaDE) uses previous experience in both trial vector generation strategies and control parameter tuning. We apply this algorithm to two design cases of dual and tri-band fllters for WiFi and WiMax applications. We select the Open Loop Ring Resonator (OLRR) fllters, which are comprised of two uniform microstrip lines and pairs of open loops between them. The results indicate the advantages of this approach and the applicability of this design method.