Logistic Map and Exponential Scaling Factor based Differential Evolution

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.

Logistic Map and Exponential Scaling Factor-Based Differential Evolution

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.

A new GA-IDW approach for interpolating the precipitation

This article presents a new algorithm for detecting the Inverse Distance Weighting Algorithm parameter (IDW) using an evolutionary technique. The algorithm was applied to interpolate 51 series of maximum annual precipitation series. Comparisons of its results with those of IDW and the optimized OIDW (a version of IDW optimized with PSO) are provided. The best performances are those of the actual approach.

Numerical Analysis of Electrohydrodynamic Flow in a Circular Cylindrical Conduit by Using Neuro Evolutionary Technique

This paper analyzes the mathematical model of electrohydrodynamic (EHD) fluid flow in a circular cylindrical conduit with an ion drag configuration. The phenomenon was modelled as a nonlinear differential equation. Furthermore, an application of artificial neural networks (ANNs) with a generalized normal distribution optimization algorithm (GNDO) and sequential quadratic programming (SQP) were utilized to suggest approximate solutions for the velocity, displacements, and acceleration profiles of the fluid by varying the Hartmann electric number (Ha2) and the strength of nonlinearity (α). ANNs were used to model the fitness function for the governing equation in terms of mean square error (MSE), which was further optimized initially by GNDO to exploit the global search. Then SQP was implemented to complement its local convergence. Numerical solutions obtained by the design scheme were compared with RK-4, the least square method (LSM), and the orthonormal Bernstein collocation method (OBCM). Stability, convergence, and robustness of the proposed algorithm were endorsed by the statistics and analysis on results of absolute errors, mean absolute deviation (MAD), Theil’s inequality coefficient (TIC), and error in Nash Sutcliffe efficiency (ENSE).

Reliability-based optimization of water distribution networks

The water distribution system serves as a basic necessity for society. Due to its large size and involvement of various components, it is one of the most expensive civil infrastructures and thus demands optimization. Much work has been done for reducing the distribution system cost. However, with only one objective, the obtained solutions may not be practical to implement. Thus, improving cost along with the efficiency of the network is the demand of the hour. The present work introduces a unique parameter-less methodology for generating Pareto fronts without involving the concept of non-dominance. The methodology incorporates the Jaya optimization model for a bi-objective problem, one being the reduction in network cost and the other is improving the reliability index of the network. The efficiency of the proposed work is analyzed for three different benchmark problems. The Jaya technique is found to be very efficient and fast when compared with the other evolutionary technique applied for the same networks. The parameter-less nature of the Jaya technique smoothens the process to a very large extent as no synchronization of algorithm parameters is required.

Semiconducting phase of hafnium dioxide under high pressure: a theoretical studied by quasi-particle GW calculations

The phase stability of the hafnium dioxide compounds HfO2, a novelmaterial with a wide range of application due to its versatility and biocompatibility,is predicted to be achievable by using evolutionary technique, based on first-principlescalculations. Herein, the candidate structure of HfO2 is revealed to adopt a tetragonalstructure under high-pressure phase with P4/nmm space group. This evidentlyconfirms the stability of the HfO2 structures, since the decomposition into thecomponent elements under pressure does not occur until the pressure is at least200GPa. Moreover, phonon calculations can confirm that the P4/nmm structure isdynamically stable. The P4/nmm structure is mainly attributed to the semiconductingproperty within using the Perdew{Burke{Ernzerhof, the modified Becke-Johnsonexchange potential in combination with the generalized gradient approximations, andthe quasi-particle GW approximation, respectively. Our calculation manifests that theP4/nmm structure likely to be metal above 200GPa, arising particularly from GWapproximation. The remarkable results of this work provide more understanding ofthe high-pressure structure for designing metal-oxide-based semiconducting materials.

Evolutionary Methods in House Floor Plan Design

In this paper, an evolutionary technique is proposed as a method for generating new design solutions for the floor layout problem. The genotypes are represented by the vectors of numerical values of points representing endpoints of room walls. Equivalents of genetic operators for such a representation are proposed. A case study of the design problem of one-story houses is presented from the initial requirements to the best solutions. An evaluation method using requirement-weighted fitness function for evolved plans is also proposed. The obtained results as well as the advantages and issues related to such an approach are also discussed.

Learning programs for the quantum computer

Manual quantum programming is generally diffcult for humans, due to the often hard-to-grasp properties of quantum mechanics and quantum computers. By outlining the target (or desired) behaviour of a particular quantum program, the task of programming can be turned into a search and optimization problem. A flexible evolutionary technique known as genetic programming may then be used as an aid in the search for quantum programs. In this work a genetic programming approach uses an estimation of distribution algorithm (EDA) to learn the probability distribution of optimal solution(s), given some target behaviour of a quantum program.

Learning programs for the quantum computer

Manual quantum programming is generally diffcult for humans, due to the often hard-to-grasp properties of quantum mechanics and quantum computers. By outlining the target (or desired) behaviour of a particular quantum program, the task of programming can be turned into a search and optimization problem. A flexible evolutionary technique known as genetic programming may then be used as an aid in the search for quantum programs. In this work a genetic programming approach uses an estimation of distribution algorithm (EDA) to learn the probability distribution of optimal solution(s), given some target behaviour of a quantum program.