Weighted Fitness Function Research Articles

An Adaptive Evolutionary Algorithm for UWB Microstrip Antennas Optimization Using a Machine Learning Technique

This article presents an application of a machine learning technique to enhance a multiobjective evolutionary algorithm to estimate fitness function behaviors from a set of experiments made in laboratory to analyze a microstrip antenna used in ultra wideband wireless devices. These function behaviors are related to three objectives: bandwidth, return loss, and central frequency deviation. Each objective is used inside an aggregate adaptive weighted fitness function that estimates the behavior in the algorithm. The machine learning technique enabled a dynamic estimation of an aggregated compound fitness function and made it possible to a prototype algorithm to learn and adapt with a set of experiments stored in a web system repository. The final results were then compared with the ones obtained with a similar antenna modeled in a simulator program and with the ones of a real prototype antenna built from the optimal values obtained after the optimization. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1864–1868, 2013

Analysis of Rule Generation Techniques using Computational Method

Problem statement: Enhanced decision making seeks a right of way mainly in business sectors for mining profitable patterns in databases . To address this problem, the authors have incorporated the temporal and utility concept of mi ning effective rules. Though the objective of generating profitable rule set is achieved. Still i n effective decision making, Optimization of the ru le set is required. Therefore the authors have used P article Swarm Optimization technique for optimizing the generated rule set. To optimize the rule set, the authors have introduced weighted fitness function, which is based on the concept of weighted support and weighted confidence. Approach: In this article the authors have done a meticulous performance study of the proposed approach Optimized UTARM also called UTARM_PSO. Also, comparative analysis of the proposed approach with the apriori_PSO technique is done. Results and Conclusion: The experimental results show that the our approach has performed th e optimization consistently and precisely on values of support ranging from 80-90%. The Comparative analysis pointed to a 15% in the overall performance of our approach against the existing ap proach.

Distributed adaptive swarm for obstacle avoidance

PurposeThe purpose of this paper is to demonstrate a novel use of a generalized Lennard‐Jones (LJ) force law in Physicomimetics, combined with offline evolutionary learning, for the control of swarms of robots moving through obstacle fields towards a goal. The paper then extends the paradigm to demonstrate the utility of a real‐time online adaptive approach named distributed agent evolution with dynamic adaptation to local unexpected scenarios (DAEDALUS).Design/methodology/approachTo achieve the best performance, the parameters of the force law used in the Physicomimetics approach are optimized, using an evolutionary algorithm (EA) (offline learning). A weighted fitness function is utilized consisting of three components: a penalty for collisions, lack of swarm cohesion, and robots not reaching the goal. Each robot of the swarm is then given a slightly mutated copy of the optimized force law rule set found with offline learning and the robots are introduced to a more difficult environment. The online learning framework (DAEDALUS) is used for swarm adaptation in this more difficult environment.FindingsThe novel use of the generalized LJ force law combined with an EA surpasses the prior state‐of‐the‐art in the control of swarms of robots moving through obstacle fields. In addition, the DAEDALUS framework allows the swarms of robots to not only learn and share behavioral rules in changing environments (in real time), but also to learn the proper amount of behavioral exploration that is appropriate.Research limitations/implicationsThere are significant issues that arise with respect to “wall following methods” and “local minimum trap” problems. “Local minimum trap” problems have been observed in this paper, but this issue is not addressed in detail. The intention is to explore other approaches to develop more robust adaptive algorithms for online learning. It is believed that the learning of the proper amount of behavioral exploration can be accelerated.Practical implicationsIn order to provide meaningful comparisons, this paper provides a more complete set of metrics than prior papers in this area. The paper examines the number of collisions between robots and obstacles, the distribution in time of the number of robots that reach the goal, and the connectivity of the formation as it moves.Originality/valueThis paper addresses the difficult task of moving a large number of robots in formation through a large number of obstacles. The important real‐world constraint of “obstructed perception” is modeled. The obstacle density is approximately three times the norm in the literature. The paper shows how concepts from population genetics can be used with swarms of agents to provide fast online adaptive learning in these challenging environments. In addition, this paper also presents a more complete set of metrics of performance.

Adaptive Scheduling Solution for Grid Meta-Brokering

The nearly optimal, interoperable utilization of various grid resources play an important role in the world of grids. Though well-designed, evaluated and widely used resource brokers have been developed, these existing solutions still cannot cope with the high uncertainty ruling current grid systems. To ease the simultaneous utilization of different middleware systems, researchers need to revise current solutions. In this paper we propose advanced scheduling techniques with a weighted fitness function for an adaptive Meta-Brokering Grid Service, which enables a higher level utilization of the existing grid brokers. We also set up a grid simulation environment to demonstrate the efficiency of the proposed meta-level scheduling solution. The presented evaluation results show that the proposed novel scheduling technique in the meta-brokering context delivers better performance.

A novel fibre design strategy for simultaneously introducing ultra small dispersion and dispersion slope using genetic algorithm

AbstractIn this paper, design of the WII triple‐clad structure as a dispersion flattened optical fibre applicable for high bit rate optical communications such as DWDM and OTDM is considered. The presented technique is based on the genetic algorithm (GA) method including a weighted fitness function. In the foregoing strategy what we believe to be a novel design methodology, a multivariables design problem is reduced to a two‐dimensional design process (σ, λ0) and this reduction is so excellent from designing point of view. The suggested weighted fitness function is capable of gathering optimal values for the dispersion and its slope simultaneously. The achieved results for the dispersion and slope at [1.5–1.6] µm wavelength interval were in [(−0.510)–(0.191)] ps/km/nm and [0.0137–0.0007] ps/km/nm2 ranges, respectively. We show that by increasing the Gaussian parameter (σ) of the weighted fitness function the dispersion reduces and becomes flattened in the wide wavelength interval. For example, in the case of σ=4.9467e‐8 the dispersion flattened broadness is larger than 120 nm. The proposed technique based on the weighted fitness function includes another design parameter, called central wavelength (λ0), which introduces additional capability to shift the zero dispersion wavelength at the arbitrary λ0 precisely. So, this fibre structure can be designed to work in each standard communication bands (S, C, and L bands). The technique put forward is capable of minimising the pulse broadening factor over the predefined interval of wavelength that is essential for large bandwidth applications. It should be mentioned that the dispersion and its slope are evaluated analytically and then the presented results have supreme precision compared to the traditional methods. Copyright © 2007 John Wiley & Sons, Ltd.

Proposal for optical fiber designs with ultrahigh effective area and small bending loss applicable to long haul communications

A proposal for the multiclad MII optical fiber structure with ultralarge effective area and small bending loss is presented. For the proposed structure small dispersion and dispersion slope are obtained thanks to what we believe to be a novel design method. The suggested design method is based on a weighted fitness function, which is applied to the genetic algorithm optimization technique. In the meantime, the foregoing structure introduces a special fiber whose mode field diameter is small and approximately insensitive to the variation of the effective area. Compared to the work reported previously, our method can precisely set the zero dispersion wavelength. The designed dispersion-shifted single-mode fibers have effective area, mode field diameter, and quality factor respectively within [150-194.79] microm(2), [6.82-7.95] microm, and [3.04-3.85] at lambda(0)=1.55 microm. An analytical method is used for the calculation of the dispersion and its slope. These calculations give dispersion and dispersion slope of [(-2.57 x 10(-4))-(-0.085)] ps/km/nm and approximately 0.064 ps/km x nm(2), respectively.

Selection of Optimal Closed-Loop Controllers for DC–DC Voltage Regulators Based on Nominal and Tolerance Design

This paper discusses a tolerance design approach for the feedback compensation networks of DC/DC switching regulators, identifying the most reliable solutions among different feasible alternatives that fulfil closed-loop design constraints. A voltage-mode-regulated DC/DC buck converter is considered as a case study. Given the performance and stability constraints, as tolerance ranges for crossover frequency and phase margin, feasible design solutions are sought by means of Monte Carlo and interval arithmetic computations. The search space is a set of available commercial values of RC parameters and related tolerances. Best design is identified by a weighted fitness function, exploring the set of solutions provided by different design approaches. The results presented in the paper highlight that the tolerance design approach allows one to find compensation networks that fit the given performance/robustness priorities better than those ones found by means of the classical nominal design approach.