Lakshman Pappula

Large array synthesis using Invasive Weed Optimization

This paper discusses the application of nature-inspired optimization techniques for the synthesis of large arrays of antennas. Here the technique of Invasive Weed Optimization (IWO) is chosen for the optimization of the amplitude and/or phase of the excitation at the individual elements of the array. The result of such optimization is performance improvement of the array in terms of minimization of sidelobe levels (SLL) and positioning of nulls at desired locations as compared to the uniformly excited linear array. It is seen that even large arrays of 100 elements can be effectively synthesized using this proposed algorithm.

Constraint-Based Synthesis of Linear Antenna Array Using Modified Invasive Weed Optimization

This paper presents a novel technique for the synthesis of unequally spaced linear antenna array. The modifled Invasive Weed Optimization (IWO) algorithm is applied to optimize the antenna element positions for suppressing peak side lobe level (PSLL) and for achieving nulls in specifled directions. The novelty of the proposed approach is in the application of a constraint-based static penalty function during optimization of the array. The static penalty function is able to put selective pressure on the PSLL, the flrst null beam width (FNBW) or the accurate null positioning as desired by the application at hand lending a high degree of ∞exibility to the synthesis process. Various design examples are considered and the obtained results are validated by comparing with the results obtained using Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Cat Swarm Optimization (CSO). Results demonstrate that the proposed method outperforms the previously published methods in terms of a signiflcant reduction in peak side lobe level while maintaining strong nulls in desired directions. The ∞exibility and ease of implementation of the modifled IWO algorithm in handling the constraints using static penalty function is evident from this analysis, showing the usefulness of the constraint based method in electromagnetic optimization problems.

Synthesis of Thinned Planar Antenna Array Using Multiobjective Normal Mutated Binary Cat Swarm Optimization

The process of thinned antenna array synthesis involves the optimization of a number of mutually conflicting parameters, such as peak sidelobe level, first null beam width, and number of active elements. This necessitates the development of a multiobjective optimization approach which will provide the best compromised solution based on the application at hand. In this paper, a novel multiobjective normal mutated binary cat swarm optimization MO-NMBCSO is developed and proposed for the synthesis of thinned planar antenna arrays. Through this method, a high degree of flexibility is introduced to the realm of thinned array design. A Pareto-optimal front containing all the probable designs is obtained in this process. Targeted solutions may be chosen from the Pareto front to satisfy the different requirements demonstrating the superiority of the proposed approach over multiobjective binary particle swarm optimization method MO-BPSO. A comparative study is carried out to quantify the performance of the two algorithms using two performance metrics.

Cat Swarm Optimization with Normal Mutation for Fast Convergence of Multimodal Functions

Abstract A normal mutation strategy based cat swarm optimization (NMCSO) that features effective global search capabilities with accelerating convergence speed is presented. The classical CSO suffers from the premature convergence and gets easily trapped in the local optima because of the random mutation process. This frailty has restricted wider range of applications of the classical CSO. To overcome the drawbacks, the normal mutation is adopted in the mutation process of this paper. It enables the cats to seek the positions in better directions by avoiding the problem of premature convergence and local optima. Experiments are conducted on several benchmark unimodal, rotated, unrotated and shifted multimodal problems to demonstrate the effectiveness of the proposed method. Furthermore, NMCSO is also applied to solve the large parameter optimization problems. The experimental results illustrate that the proposed method is quite superior to classical CSO, particle swarm optimization (PSO) and some of the state of the art evolutionary algorithms in terms of convergence speed, global optimality, solution accuracy and algorithm reliability.

Unequally spaced linear antenna array synthesis using multi-objective cauchy mutated cat swarm optimization

The process of synthesis of antenna array involves the simultaneous optimization of the conflict parameters such as peak sidelobe level (PSLL) and first null beamwidth (FNBW). In this paper, the multi-objective form of the newly developed Cauchy mutated cat swarm optimization (CMCSO) is developed and proposed to trade-off the PSLL and FNBW. A Pareto-optimal front containing all the compromised solutions is developed using a multi-objective (MO) form of CMCSO, traditional cat swarm optimization (CSO) and particle swarm optimization (PSO). Targeted compromised solutions may be chosen from the optimal Pareto front based on the application at hand. Numerical illustrations show that MO-CMCSO yields superior results as compared to MO-CSO and MO-PSO.

Planar thinned antenna array synthesis using multi-objective binary cat swarm optimization

The process of thinned antenna array synthesis involves the simultaneous minimization of a number of mutually conflicting parameters, such as peak sidelobe level and first null beam width. This necessitates the development of a multi objective optimization approach which will provide the best compromised solution based on the application at hand. In this paper multi-objective optimization is achieved using multi-objective binary cat swarm optimization (MOBCSO). A Pareto-optimal front containing all the probable designs is obtained in this process. Targeted solutions may be chosen from the Pareto front to satisfy the different requirements demonstrating the superiority of the proposed approach over multi objective binary particle swarm optimization method (MOBPSO).

Synthesis of aperiodic linear antenna array using multi-objective cat swarm optimization

Pareto optimal synthesis of aperiodic linear antenna array using cat swarm optimization is proposed in this paper. The synthesis of aperiodic antenna array is a highly nonlinear problem and multi-objective in nature, in which the two dissension parameters like peak sidelobe level (PSLL) and first null beam width (FNBW) have to be minimized simultaneously. To solve the aforementioned problem, multi-objective cat swarm optimization (MOCSO) is proposed to determine the compromised solutions of the dissension parameters PSLL and FNBW by optimizing the antenna element positions. Specific solutions may be chosen from the obtained Pareto optimal set to demonstrate the effectiveness of the MOCSO method over other existed multi-objective methods.

Sparse antenna array synthesis using multi-objective optimization

The process of sparse antenna array synthesis involves the simultaneous minimization of the number of mutually conflicting parameters, such as peak sidelobe level and first null beam width. This necessitates the development of a multi objective optimization process which will provide the best compromised solution based on the application at hand. In this paper multi-objective optimization is achieved using the non-dominating sorting genetic algorithm of NSGA-II. This approach yields much more improved results as compared to single objective optimization approach and at the same time it offers flexibility in choosing the solution based on the Pareto front.