Antenna optimization using an evolutionary programming algorithm with a hybrid mutation operator

Previous studies have shown that embedding local search in classical evolutionary programming (EP) could lead to improved performance on function optimization problems. The utility of local search is investigated with fast evolutionary programming (FEP) and comparisons are offered between performance improvements obtained when using local search with Gaussian and Cauchy mutations. Experiments were conducted on a suite of four well known function optimization problems using two local search methods (conjugate gradient and F.J. Solis and R.J.-B. Wets, (1981)) with varying amounts of local search being incorporated into the evolutionary algorithm. Empirical results indicate that FEP with the conjugate gradient method outperforms other hybrid methods on three of the four functions when evolution was conducted for a fixed number of generations. Trials using local search produced solutions that were statistically as good as or better than trials without local search. However, the cost of using local search justified the enhancement in solution quality only when using Gaussian mutations but not when using Cauchy mutations.

Fast evolutionary programming techniques for short-term hydrothermal scheduling

Different mutation operators such as Gaussian, Cauchy and Levy mutations have been proposed in evolutionary programming. According to the no free lunch theorem, operators are only efficient within certain fitness landscapes. Therefore the mixed strategy, integrating several mutation operators into a single algorithm, is a nature development in order to combine the advantages of different operators. Based on Shapley value, this paper presents a new mixed strategy evolutionary programming algorithm. It employs Gaussian, Cauchy and Levy mutation operators and uses Shapley value to assign weights to these three operators. Then evolutionary programming using the new mixed strategy is tested on a set of 22 benchmark problems. The performance of the new mixed strategy is compared with other two mixed mutation strategies and three pure strategies. The experimental results show that the new mixed strategy has achieved an acceptable accuracy.

Evolutionary programming (EP) has been successfully applied to many parameter optimization problems. We propose a mean mutation operator, consisting of a linear combination of Gaussian and Cauchy mutations. Preliminary results indicate that both the adaptive and non-adaptive versions of the mean mutation operator are capable of producing solutions that are as good as, or better than those produced by Gaussian mutations alone. The success of the adaptive operator could be attributed to its ability to self-adapt the shape of the probability density function that generates the mutations during the run.

Evolutionary Programming (EP) and Differential Evolution (DE) are well known as simple and efficient schemes for global optimization over continuous spaces. Both EP and DE use mutation for producing offspring. The mutation operators of EP usually generate the search step size for mutation by probability distribution functions while the mutation operators of DE generate it by adding a weighted difference vector between two individuals to a third individual. In this paper, a new EP algorithm is proposed based on dual mutation strategy (DMEP) as it incorporates both the mutation operators of EP and DE literature. Thus the balance between exploration and exploitation is obtained by two different categories of mutation operators. To evaluate the performance of the proposed scheme, a test-suite of 37 benchmark functions has been used and results have been compared with some prominent evolutionary systems. Experimental results show the remarkable effectiveness of the dual mutation strategy employed by DMEP.

This article presents a solution to the hydrothermal scheduling (HS) problem by evolutionary programming (EP). The purpose of HS is to minimize fuel costs of thermal units by allocating hydro and thermal units optimally, satisfying constraints on system operation. EP performs better than conventional methods in converging to near optimum results in the solution of the HS problem, which consists of nonlinear objective function and constraints. In EP, offsprings are generated from randomly generated initial parent vectors by Gauss or Cauchy mutations. Parent vectors and their offspring vectors compete with each other. Better individuals are selected as new parent vectors for the next iteration. As the iteration makes progress, convergence to optimum solution increases. In this study, instead of stochastic competition in existing EP algorithms, deterministic competition is utilized. The control parameter “scaling factor” is taken as variable instead of constant. Thus, better results have been obtained in convergence rate, solution time and success rate.

The design of evolutionary computations based on schema processing, minimizing expected losses, and emphasizing certain genetic operators has failed to provide robust optimization performance. Recently, fitness distribution analysis has been proposed as an alternative tool for exploring operator behavior and designing efficient evolutionary computations. For example, the step size of a single parent variation operator, such as the Gaussian mutation operator, determines the corresponding probability of finding better solutions and the expected improvement that will be obtained. The paper analyses the utility of Gaussian, Cauchy, and mean mutation operators when a parent is located near a local extrema of a continuous objective function that is to be optimized.

Moth flame optimization (MFO) algorithm proves to be an excellent choice for numerical optimization. However, for some complex objectives, MFO may get trapped in local optima or suffer from premature convergence. In order to overcome these issues, an improved MFO-based algorithm, called opposition-based moth flame optimization with Cauchy mutation and evolutionary boundary constraint handling (OMFO), is presented. The proposed method integrates opposition-based learning (OBL) with Cauchy mutation (CM) and evolution boundary constraint handling (EBCH) technique with MFO to improve its performance. OBL and EBCH improve the convergence of MFO, while CM helps MFO to escape local optima. The effect of each method (OBL, CM, EBCH) on MFO is validated using 18 benchmark functions and two constrained real-world problems. Simulation results indicate that opposition-based MFO integrated with Cauchy mutation and EBCH has the best performance among the MFO variants. The OMFO algorithm is also compared with various algorithms in the literature and provides competitive results in terms of increased exploitation and exploration capability, improved convergence and local optima avoidance.

This paper provides an analysis of the application of Cauchy mutation and Gaussian mutation in one of the modified version of the popular and well known particle swarm optimization technique. The main objective is to provide better convergence as well finest results in the solutions of various real world applications. The particle swarm optimization is generally acted as a foundation in the area of swarm intelligence. The modified PSO used a improved weight factor to provide a better convergence than the conventional PSO. But many real world problems need a further refinement in the solution. Therefore, in this paper two types of well known mutations are used with the modified PSO for betterment in the refining process. The proposed mutations are Gaussian mutation and Cauchy mutation. The proposed mutated modified PSO algorithms are simulated by the use of standard benchmarking functions to establish their dominance over the already modified PSO. Between Cauchy MPSO and Gaussian MPSO, the overall performance of Cauchy MPSO is better. The use of Cauchy mutation as well as Gaussian mutation in the modified PSO helps in the improvement of the convergence of the modified PSO for approaching much better solutions.

This paper is dealing with the solution of the hydrothermal scheduling problem by using evolutionary algorithm. The aim of this study is to minimize the fuel cost and ramping cost of thermal units by utilizing both hydro and thermal units optimally. In evolutionary programming, new generations are produced from randomly generated initial vector by Gauss and Cauchy mutations and they compete with parent vectors and each other. Better individuals are selected for the next generation. The new generation process lasts until either reach to defined iteration number or a minimum function value or the point where developed solutions are no longer different.

In this paper, we review recent advances in evolutionary programming (EP) and its implementation in various antenna, microwave, frequency selective surfaces and RF circuit optimization problems. EP, unlike the other two paradigms of evolutionary computational techniques, namely, genetic algorithms (GA) and evolution strategies (ES), uses a mutation only variation operator where adaptive and/or self-adaptive techniques exist, or can easily be designed, for adapting the parameters of mutation operator during the evolution process. We present the so-called meta-EP and design of its mutation operator, using Gaussian, Cauchy and Poisson mutations as well as a hybrid of these mutations, for continuous, discrete and mixed parameter electromagnetic optimization problems. In addition, an efficient hybrid use of EP, gradient search methods and cluster analysis, as well as a novel hybrid EP-GA algorithm are discussed. Examples presented include optimizations of corrugated horn antennas, multilayered stacked microstrip antennas, Yagi-Uda arrays, partially reflective surfaces, dielectric filters, meander-line polarizer and RF duplexers

Scheduling algorithm based on evolutionary computing in identical parallel machine production line

Cauchy mutation for decision-making variable of Gaussian particle swarm optimization applied to parameters selection of SVM

The mutation operator is the only source of variation in Evolutionary Programming. In the past these have been human nominated and included the Gaussian, Cauchy, and the Levy distributions. We automatically design mutation operators (probability distributions) using Genetic Programming. This is done by using a standard Gaussian random number generator as the terminal set and and basic arithmetic operators as the function set. In other words, an arbitrary random number generator is a function of a randomly (Gaussian) generated number passed through an arbitrary function generated by Genetic Programming. Rather than engaging in the futile attempt to develop mutation operators for arbitrary benchmark functions (which is a consequence of the No Free Lunch theorems), we consider tailoring mutation operators for particular function classes. We draw functions from a function class (a probability distribution over a set of functions). The mutation probability distribution is trained on a set of function instances drawn from a given function class. It is then tested on a separate independent test set of function instances to confirm that the evolved probability distribution has indeed generalized to the function class. Initial results are highly encouraging: on each of the ten function classes the probability distributions generated using Genetic Programming outperform both the Gaussian and Cauchy distributions.

Abstract: P243 ROLE OF IMMUNOGLOBULIN-LIKE CELL ADHESION MOLECULE NECL-5 IN VEGF-INDUCED ANGIOGENESIS