Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang–Ball curves

Distributed generation (DG) systems are integral parts in future distribution networks. In this paper, a novel approach integrating crisscross optimization algorithm and Monte Carlo simulation (CSO-MCS) is implemented to solve the optimal DG allocation (ODGA) problem. The feature of applying CSO to address the ODGA problem lies in three interacting operators, namely horizontal crossover, vertical crossover and competitive operator. The horizontal crossover can search new solutions in a hypercube space with a larger probability while in the periphery of each hypercube with a decreasing probability. The vertical crossover can effectively facilitate those stagnant dimensions of a population to escape from premature convergence. The competitive operator allows the crisscross search to always maintain in a historical best position to quicken the converge rate. It is the combination of the double search strategies and competitive mechanism that enables CSO significant advantage in convergence speed and accuracy. Moreover, to deal with system uncertainties such as the output power of wind turbine and photovoltaic generators, an MCS-based method is adopted to solve the probabilistic power flow. The effectiveness of the CSO-MCS method is validated on the typical 33-bus and 69-bus test system, and results substantiate the suitability of CSO-MCS for multi-objective ODGA problem.

Crisscross optimization algorithm and its application

With the increasing emphasis on energy conservation and environmental protection, fuel cost and emission should be both considered into economic dispatch. The multi-area economic/emission dispatch (MOMAED) is a complex non-convex multi-objective problem, which is not yet addressed well. Aiming at this problem, this paper proposes an efficient and powerful crisscross optimization algorithm (CSO) combining Pareto multi-objective processing strategy(MOCSO). The proposed MOCSO approach addresses the different types of complex MOMAED problem by applying two interacting search operators, namely horizontal crossover and vertical crossover. The former enhances MOCSO's global search ability in complex solution space by introducing a distinctive cross-border crossover mechanism while the latter prevents the premature convergence by a unique dimensional crossover approach. Both operators generate moderation solutions and always maintain a population of currently personal best solutions through a greedy update strategy so as to speed up the process of finding the optimal solution. The proposed approach is validated on two test systems. The effectiveness and efficiency of the MOCSO algorithm is first tested on the MOMAED problems. The results show that the proposed MOCSO method yields higher quality solutions than other methods in the literature.

It is crucial for the photovoltaic system to have an accurate model and well‐estimated parameters to further increase conversion efficiency. Most existing methods for identifying photovoltaic model parameters have problems such as high computational cost, local optimum trouble, or difficulty in providing the best performance due to complex adjustments of algorithm parameters. To improve these defects, a hunter‐prey optimization algorithm coordinating mutual benefit and sharing and interactive learning activities (EHPO) is proposed. First, based on hunter‐prey optimization, timely information sharing with the mutual benefit and sharing mechanism was achieved when the algorithm was applied to search for prey, thus improving the searching precision and convergence rate of the algorithm. Second, hunters used the history optimization information and useful information from peers to guide the search direction, thus balancing the global and local development abilities of the algorithm. Finally, a lens imaging backward learning strategy was adopted to prevent the algorithm from falling into the local optimum, thus increasing the diversity of varieties and the probability of finding the global optimal solution. The simulation results of the single‐diode model (SDM), double‐diode model (DDM), triple‐diode model (TDM), and other PV models in different environmental conditions show that the improved EHPO algorithm is more advantageous for parameter extraction than other advanced metaheuristic algorithms. This study demonstrates that EHPO is an accurate and reliable tool for predicting the parameters of PV models.

A general-purpose optimization program for engineering design

In the paper software package called Computer Aided Multicriterion Optimization System (CAMOS) is briefly described. The system enables the designer to solve single and multicriterion optimization problems for nonlinear programming models with continuous, integer, discrete and mixed design variables. The system is designed to facilitate the interactive processes considering both input/output information arrangement and multicriterion decision making problems. Several engineering design examples are presented to show the advantages of the use of the system. These examples deal with design and optimization of helical springs, hydrostatic journal bearings, helical gearsets multiple clutch brakes. Multicriterion optimization models for all the examples are briefly described and the computer aided design sessions with CAMOS are presented. The structure of CAMOS and the subroutine which describes the design problem are arranged in the way that the analysis, optimization and decision making phases are one entity. The analysis and decision making phases can be supported by a graphical illustration of the problem formulation and the solution obtained. Both the system and problem oriented computer aided optimum design modules are coded in FORTRAN and prepared for an IBM PC/XT/AT.

A RFBSE model for capturing engineers’ useful knowledge and experience during the design process

SUMMARY Two different kinds of information, i.e. qualitative and quantitative, are currently involved in the implementation of an engineering design. However, qualitative information has always been excluded from the numerical design optimization process, so that the solution obtained was actually suboptimal. A qualitative programming method for the design decision-making problem is described in the present paper, making use of qualitative information which has been obtained during the conceptual design stage of engineering design. Qualitative information as well as incomplete information could be considered during the design process by applying the qualitative programming method. Two engineering design examples, i.e. structural design and industrial design, are demonstrated to illustrate the application capability of the proposed approach.

Machine Learning in Engineering Analysis and Design: An Integrated Fuzzy Neural Network Learning Model

Abstract— One of the main problems underlying most optimization theories is local optimum. When time delays are presented, this issue becomes much more problematic. In such conditions, evolutionary optimization algorithms are proven to be helpful. In this paper, quantum genetic algorithm (QGA) has been used to tackle the stated problem in the framework of delay-dependent linear matrix inequality (LMI) robust H∞ control. QGA is employed to find suitable feedback gains and delay-dependent LMI solvers are concerned to resolve stability issues. In addition, to provide more balance between exploration and exploitation, to increase convergence rate as well as to prevent premature convergence, it is proposed that particle swarm optimization (PSO) is augmented with QGA. Simulation is dealt with LMI-based H∞ control scheme of the QGA and QGA-PSO optimization space from the design point of one-degree freedom single link scara robot. The whole controller satisfies the desired properties for uncertain-but-known constant bounded time delay. Furthermore, one of the drawbacks found in tests of most hybrid global-local strategies, i.e. premature convergence, has been cancelled by the proposed scheme of QGA and PSO.

This paper presents a powerful swarm intelligence meta-heuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization. The original Cat Swarm Optimization suffers from the shortcoming of 'premature convergence', which is the possibility of entrapment in local optima which usually happens due to the off-balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real world scenario are used. In addition, the Dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion as well as future directions to further improve the algorithm are discussed.

Aiming at the defects of the Aquila optimizer (AO) in dealing with some complex optimization problems, such as slow convergence speed, low convergence accuracy, and easy to fall into local optimum, in this paper, a hybrid Aquila optimizer (HAO) algorithm based on Gauss map and crisscross operator is proposed. First, Gauss map is introduced to initialize the Aquila population to improve the quality of the initial population. Then use the crisscross operator to promote the exchange of information within the population and maintain the diversity of the population in each iteration, which not only enhances the ability of the algorithm to jump out of the local optimum but also accelerates the global convergence of the algorithm. The results of experiments using 21 classical benchmark functions indicate that HAO has better global search ability, faster convergence speed, and better stability than AO. The overall optimization performance of HAO in different dimensions is better than particle swarm optimization (PSO) algorithm, gray wolf optimization (GWO) algorithm, whale optimization algorithm (WOA), and crisscross optimization (CSO) algorithm. Finally, the results of K-means clustering optimization on six University of California (UCI) standard data sets demonstrate that HAO has significant advantages over three algorithms that are good at clustering optimization.

Surrogate models or metamodels are commonly used to exploit expensive computational simulations within a design optimization framework. The application of multi-fidelity surrogate modeling approaches has recently been gaining ground due to the potential for further reductions in simulation effort over single fidelity approaches. However, given a black box problem when exactly should a designer select a multi-fidelity approach over a single fidelity approach and vice versa? Using a series of analytical test functions and engineering design examples from the literature, the following paper illustrates the potential pitfalls of choosing one technique over the other without a careful consideration of the optimization problem at hand. These examples are then used to define and validate a set of guidelines for the creation of a multi-fidelity Kriging model. The resulting guidelines state that the different fidelity functions should be well correlated, that the amount of low fidelity data in the model should be greater than the amount of high fidelity data and that more than 10 % and less than 80 % of the total simulation budget should be spent on low fidelity simulations in order for the resulting multi-fidelity model to perform better than the equivalent costing high fidelity model.

Dynamic diversity control in genetic algorithm for mining unsearched solution space in TSP problems

As demonstrated in Chap. 3, different evolutionary algorithms, including genetic algorithm (GA), simulated annealing (SA) algorithm, hybrid GASA algorithm, particle swarm optimization (PSO) algorithm, continuous ant colony optimization (CACO) algorithm, artificial bee colony (ABC) algorithm, and immune algorithm (IA), are employed to determine suitable for parameter combination of an SVR-based electric load forecasting model. These forecasting results indicate that almost all SVR-based models with different evolutionary algorithms are superior to other competitive forecasting models (including ARIMA, HW, GRNN, and BPNN models); however, these algorithms almost lack knowledge memory or storage mechanisms which would be neither time-consuming nor inefficient in searching the suitable parameters, that is, premature convergence (being trapped in local optimum). For example, for SVRGA model, in GA processing, new individuals are generated by the following operators, selection, crossover, and mutation. For all types of objective functions, the generation begins with a binary coding for the parameter set. Based on this special binary coding process, GA is able to solve some specified problems which are not easily to be solved by traditional algorithms. GA can empirically provide a few best fitted offsprings from the whole population; however, after some generations, due to low diversity of the population, it might lead to a premature convergence [1]. For SVRSA model, SA algorithm is a generic probabilistic search technique that simulates the material physical process of heating and controlled cooling. Each step of SA attempts to replace the current state by a random move. The new state may then be accepted with a probability that depends both on the difference between the corresponding function values and also on a global parameter, temperature. Thus, SA has some institution to reach more ideal solutions. However, SA algorithm requires the subtle and skillful adjustment in the annealing schedule; therefore, the settings of the size of the temperature steps during annealing, the temperature range, the number of restarts, and the redirection of the search will be taken into account carefully [1]; in addition, its Monte Carlo scheme and lack of knowledge memory mechanism will also lead to time-consuming and inefficient searching in annealing process.