5,375 research outputs found
Comparison of Evolutionary Optimization Algorithms for FM-TV Broadcasting Antenna Array Null Filling
Broadcasting antenna array null filling is a very
challenging problem for antenna design optimization. This paper
compares five antenna design optimization algorithms (Differential
Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive
Invasive Weed) as solutions to the antenna array null filling
problem. The algorithms compared are evolutionary algorithms
which use mechanisms inspired by biological evolution, such as
reproduction, mutation, recombination, and selection. The focus of
the comparison is given to the algorithm with the best results,
nevertheless, it becomes obvious that the algorithm which produces
the best fitness (Invasive Weed Optimization) requires very
substantial computational resources due to its random search
nature
Optimal Wideband LPDA Design for Efficient Multimedia Content Delivery over Emerging Mobile Computing Systems
An optimal synthesis of a wideband Log-Periodic
Dipole Array (LPDA) is introduced in the present study. The LPDA optimization is performed under several requirements concerning the standing wave ratio, the forward gain, the gain flatness, the front-to-back ratio and the side lobe level, over a
wide frequency range. The LPDA geometry that complies with the above requirements is suitable for efficient multimedia content delivery. The optimization process is accomplished by applying a recently introduced method called Invasive Weed Optimization (IWO). The method has already been compared to other evolutionary methods and has shown superiority in solving complex non-linear problems in telecommunications and electromagnetics. In the present study, the IWO method has been chosen to optimize an LPDA for operation in the frequency range
800-3300 MHz. Due to its excellent performance, the LPDA can effectively be used for multimedia content reception over future mobile computing systems
Nature-inspired Cuckoo Search Algorithm for Side Lobe Suppression in a Symmetric Linear Antenna Array
In this paper, we proposed a newly modified cuckoo search (MCS) algorithm integrated with the Roulette wheel selection operator and the inertia weight controlling the search ability towards synthesizing symmetric linear array geometry with minimum side lobe level (SLL) and/or nulls control. The basic cuckoo search (CS) algorithm is primarily based on the natural obligate brood parasitic behavior of some cuckoo species in combination with the Levy flight behavior of some birds and fruit flies. The CS metaheuristic approach is straightforward and capable of solving effectively general N-dimensional, linear and nonlinear optimization problems. The array geometry synthesis is first formulated as an optimization problem with the goal of SLL suppression and/or null prescribed placement in certain directions, and then solved by the newly MCS algorithm for the optimum element or isotropic radiator locations in the azimuth-plane or xy-plane. The study also focuses on the four internal parameters of MCS algorithm specifically on their implicit effects in the array synthesis. The optimal inter-element spacing solutions obtained by the MCS-optimizer are validated through comparisons with the standard CS-optimizer and the conventional array within the uniform and the Dolph-Chebyshev envelope patterns using MATLABTM. Finally, we also compared the fine-tuned MCS algorithm with two popular evolutionary algorithm (EA) techniques include particle swarm optimization (PSO) and genetic algorithms (GA)
Multiobjective optimization of electromagnetic structures based on self-organizing migration
PrĂĄce se zabĂœvĂĄ popisem novĂ©ho stochastickĂ©ho vĂcekriteriĂĄlnĂho optimalizaÄnĂho algoritmu MOSOMA (Multiobjective Self-Organizing Migrating Algorithm). Je zde ukĂĄzĂĄno, ĆŸe algoritmus je schopen ĆeĆĄit nejrĆŻznÄjĆĄĂ typy optimalizaÄnĂch Ășloh (s jakĂœmkoli poÄtem kritĂ©riĂ, s i bez omezujĂcĂch podmĂnek, se spojitĂœm i diskrĂ©tnĂm stavovĂœm prostorem). VĂœsledky algoritmu jsou srovnĂĄny s dalĆĄĂmi bÄĆŸnÄ pouĆŸĂvanĂœmi metodami pro vĂcekriteriĂĄlnĂ optimalizaci na velkĂ© sadÄ testovacĂch Ășloh. Uvedli jsme novou techniku pro vĂœpoÄet metriky rozprostĆenĂ (spread) zaloĆŸenĂ© na hledĂĄnĂ minimĂĄlnĂ kostry grafu (Minimum Spanning Tree) pro problĂ©my majĂcĂ vĂce neĆŸ dvÄ kritĂ©ria. DoporuÄenĂ© hodnoty pro parametry ĆĂdĂcĂ bÄh algoritmu byly urÄeny na zĂĄkladÄ vĂœsledkĆŻ jejich citlivostnĂ analĂœzy. Algoritmus MOSOMA je dĂĄle ĂșspÄĆĄnÄ pouĆŸit pro ĆeĆĄenĂ rĆŻznĂœch nĂĄvrhovĂœch Ășloh z oblasti elektromagnetismu (nĂĄvrh Yagi-Uda antĂ©ny a dielektrickĂœch filtrĆŻ, adaptivnĂ ĆĂzenĂ vyzaĆovanĂ©ho svazku v ÄasovĂ© oblastiâŠ).This thesis describes a novel stochastic multi-objective optimization algorithm called MOSOMA (Multi-Objective Self-Organizing Migrating Algorithm). It is shown that MOSOMA is able to solve various types of multi-objective optimization problems (with any number of objectives, unconstrained or constrained problems, with continuous or discrete decision space). The efficiency of MOSOMA is compared with other commonly used optimization techniques on a large suite of test problems. The new procedure based on finding of minimum spanning tree for computing the spread metric for problems with more than two objectives is proposed. Recommended values of parameters controlling the run of MOSOMA are derived according to their sensitivity analysis. The ability of MOSOMA to solve real-life problems from electromagnetics is shown in a few examples (Yagi-Uda and dielectric filters design, adaptive beam forming in time domainâŠ).
Comparison of Weighted Sum Fitness Functions for PSO Optimization of Wideband Medium-gain Antennas
In recent years PSO (Particle Swarm Optimization) has been successfully applied in antenna design. It is well-known that the cost function has to be carefully chosen in accordance with the requirements in order to reach an optimal result. In this paper, two different wideband medium-gain arrays are chosen as benchmark structures to test the performance of four PSO fitness functions that can be considered in such a design. The first one is a planar 3 element, the second one a linear 4 element antenna. A MoM (Method of Moments) solver is used in the design. The results clearly show that the fitness functions achieve a similar global best candidate structure. The fitness function based on realized gain however converges slightly faster than the others
- âŠ