Optimization of roundness error in deep hole drilling using cuckoo search algorithm

In the manufacturing industry, machining is a part of all manufacture in almost all metal products. Machining of holes is one of the most common processes in the manufacturing industries. Deep hole drilling, DHD is classified as a complex machining process .This study presents an optimization of machining parameters in DHD using Cuckoo Search algorithm, CS comprising feed rate (f), spindle speed (s), depth of hole (d) and Minimum Quantity Lubrication MQL, (m). The machining performance measured is roundness error, Re. The real experimentation was designed based on Design of Experiment, DoE which is two levels full factorial with an added centre point. The experimental results were used to develop the mathematical model using regression analysis that used in the optimization process. Analysis of variance (ANOVA) and Fisher‘s statistical test (F-test) are used to check the significant of the model developed. According to the results obtained by experimental the minimum value of Re is 0.0222μm and by CS is 0.0198μm. For the conclusion, it was found that CS is capable of giving the minimum value of Re as it outperformed the result from the experimental

Nature-inspired Cuckoo Search Algorithm for Side Lobe Suppression in a Symmetric Linear Antenna Array

In this paper, we proposed a newly modified cuckoo search (MCS) algorithm integrated with the Roulette wheel selection operator and the inertia weight controlling the search ability towards synthesizing symmetric linear array geometry with minimum side lobe level (SLL) and/or nulls control. The basic cuckoo search (CS) algorithm is primarily based on the natural obligate brood parasitic behavior of some cuckoo species in combination with the Levy flight behavior of some birds and fruit flies. The CS metaheuristic approach is straightforward and capable of solving effectively general N-dimensional, linear and nonlinear optimization problems. The array geometry synthesis is first formulated as an optimization problem with the goal of SLL suppression and/or null prescribed placement in certain directions, and then solved by the newly MCS algorithm for the optimum element or isotropic radiator locations in the azimuth-plane or xy-plane. The study also focuses on the four internal parameters of MCS algorithm specifically on their implicit effects in the array synthesis. The optimal inter-element spacing solutions obtained by the MCS-optimizer are validated through comparisons with the standard CS-optimizer and the conventional array within the uniform and the Dolph-Chebyshev envelope patterns using MATLABTM. Finally, we also compared the fine-tuned MCS algorithm with two popular evolutionary algorithm (EA) techniques include particle swarm optimization (PSO) and genetic algorithms (GA)

Multilevel Thresholding for Image Segmentation Using an Improved Electromagnetism Optimization Algorithm

Image segmentation is considered one of the most important tasks in image processing, which has several applications in different areas such as; industry agriculture, medicine, etc. In this paper, we develop the electromagnetic optimization (EMO) algorithm based on levy function, EMO-levy, to enhance the EMO performance for determining the optimal multi-level thresholding of image segmentation. In general, EMO simulates the mechanism of attraction and repulsion between charges to develop the individuals of a population. EMO takes random samples from search space within the histogram of image, where, each sample represents each particle in EMO. The quality of each particle is assessed based on Otsu’s or Kapur objective function value. The solutions are updated using EMO operators until determine the optimal objective functions. Finally, this approach produces segmented images with optimal values for the threshold and a few number of iterations. The proposed technique is validated using different standard test images. Experimental results prove the effectiveness and superiority of the proposed algorithm for image segmentation compared with well-known optimization methods

Optimum design of reinforced concrete retaining walls with the flower pollination algorithm

The flower pollination algorithm (FPA) is anefficient metaheuristicoptimizationalgorithm mimickingthe pollinationprocessof flowering species. In this study, FPA is applied, for first time, to the optimum design of reinforced concrete (RC) cantilever retaining walls. It is foundthat FPA offers important savings with respect to conventional design approachesand that it outperformsgenetic algorithm (GA)andthe particle swarm optimization (PSO) algorithm in this designproblem.Furthermore, parameter tuning reveals that the best FPA performance is achieved for switch probability values ranging between 0.4 and 0.7, a population size of 20 individualsand aLévy flightstep sizescale factor of 0.5. Finally, parametric optimum designs show that theoptimumcost of RC retaining walls increases rapidly with the wallheight and smoothly with the magnitude of surcharge loadin

PENGEMBANGAN metaheuristicOpt: R PACKAGE UNTUK OPTIMASI DENGAN MENGGUNAKAN ALGORITMA POPULATION BASED METAHEURISTIC

Optimasi diterapkan diberbagai disiplin ilmu seperti teknik sipil, teknik mekanika, ekonomi, teknik elektro dan lain-lain. Karena optimasi diterapkan diberbagai disiplin ilmu maka optimasi sangatlah penting. Banyak sekali pendekatan yang dilakukan dalam melakukan optimasi salah satunya adalah population based metaheuristic. Di bahasa pemrograman R terdapat package optimasi menggunakan algoritma population based metaheuristic yaitu “metaheuristicOpt”. Algoritma-algoritma pada R package “metaheuristicOpt” memiliki dua kelemahan yaitu kompleksitas yang tinggi dan hyperparameter yang sedikit. Tujuan penelitian ini adalah mengembangkan R package “metaheuristicOpt” dengan menambahkan 10 algoritma baru yaitu clonal selection algorithm, differential evolution, shuffled frog leaping, cat swarm optimization, artificial bee colony algorithm, krill herd algorithm, cuckoo search, bat algorithm, gravitational based search dan black hole optimization untuk menutupi kelemahan algoritma sebelumnya. Dalam menambahkan algoritma ini kami menjaga konsistensi arsitektur package tersebut. Untuk menganalisis performa dari algoritma baru yang ditambahkan setiap fungsi diuji menggunakan 13 fungsi uji. Yang menjadi tolok ukur eksperimen adalah fitness dan waktu eksekusi. Berdasarkan eksperimen yang dilakukan beberapa algoritma baru memiliki kecepatan eksekusi yang lebih cepat dari algoritma sebelumnya dan beberapa algoritma baru juga memiliki fitness yang lebih baik dari algoritma sebelumnya. Optimization is applied in various scientific disciplines such as civil engineering, mechanical engineering, economics, electrical engineering and others. Because optimization is applied in various disciplines, optimization is very important. There are a lot of approaches used to optimize one of them is population based metaheuristic. In the R programming language there is an optimization package using the population based metaheuristic algorithm, namely "metaheuristicOpt". Algorithms in the R package "metaheuristicOpt" have two disadvantages: high complexity and few hyperparameters. Our goal is to develop the "metaheuristicOpt" package by adding 10 new algorithms namely clonal selection algorithm, differential evolution, shuffled frog leaping, cat swarm optimization, artificial bee colony algorithm, krill herd algorithm, cuckoo search, bat algorithm, gravitational based search and black hole optimization to cover up the weaknesses of the previous algorithm. In adding of these algorithms we maintain the consistency of the package architecture. To analyse performance of the new algorithm added to each function of the algorithm, experiments were carried out using 13 test functions. The benchmarks of the experiment are fitness and execution time. Based on experiments some of new algorithms added have a faster execution speed and better fitness than the previous algorithms

Flower pollination algorithm with pollinator attraction

The Flower Pollination Algorithm (FPA) is a highly efficient optimization algorithm that is inspired by the evolution process of flowering plants. In the present study, a modified version of FPA is proposed accounting for an additional feature of flower pollination in nature that is the so-called pollinator attraction. Pollinator attraction represents the natural tendency of flower species to evolve in order to attract pollinators by using their colour, shape and scent as well as nutritious rewards. To reflect this evolution mechanism, the proposed FPA variant with Pollinator Attraction (FPAPA) provides fitter flowers of the population with higher probabilities of achieving pollen transfer via biotic pollination than other flowers. FPAPA is tested against a set of 28 benchmark mathematical functions, defined in IEEE-CEC’13 for real-parameter single-objective optimization problems, as well as structural optimization problems. Numerical experiments show that the modified FPA represents a statistically significant improvement upon the original FPA and that it can outperform other state-of-the-art optimization algorithms offering better and more robust optimal solutions. Additional research is suggested to combine FPAPA with other modified and hybridized versions of FPA to further increase its performance in challenging optimization problems

An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems

Flower pollination algorithm parameters tuning

The flower pollination algorithm (FPA) is a highly efficient metaheuristic optimization algorithm that is inspired by the pollination process of flowering species. FPA is characterised by simplicity in its formulation and high computational performance. Previous studies on FPA assume fixed parameter values based on empirical observations or experimental comparisons of limited scale and scope. In this study, a comprehensive effort is made to identify appropriate values of the FPA parameters that maximize its computational performance. To serve this goal, a simple non-iterative, single-stage sampling tuning method is employed, oriented towards practical applications of FPA. The tuning method is applied to the set of 28 functions specified in IEEE-CEC'13 for real-parameter single-objective optimization problems. It is found that the optimal FPA parameters depend significantly on the objective functions, the problem dimensions and affordable computational cost. Furthermore, it is found that the FPA parameters that minimize mean prediction errors do not always offer the most robust predictions. At the end of this study, recommendations are made for setting the optimal FPA parameters as a function of problem dimensions and affordable computational cost. [Abstract copyright: © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.

A Pareto-Based Sensitivity Analysis and Multiobjective Calibration Approach for Integrating Streamflow and Evaporation Data

Evaporation is gaining increasing attention as a calibration and evaluation variable in hydrologic studies that seek to improve the physical realism of hydrologic models and go beyond the long-established streamflow-only calibration. However, this trend is not yet reflected in sensitivity analyses aimed at determining the relevant parameters to calibrate, where streamflow has traditionally played a leading role. On the basis of a Pareto optimization approach, we propose a framework to integrate the temporal dynamics of streamflow and evaporation into the sensitivity analysis and calibration stages of the hydrologic modeling exercise, here referred to as “Pareto-based sensitivity analysis” and “multiobjective calibration.” The framework is successfully applied to a case study using the Variable Infiltration Capacity (VIC) model in three catchments located in Spain as representative of the different hydroclimatic conditions within the Iberian Peninsula. Several VIC vegetation parameters were identified as important to the performance estimates for evaporation during sensitivity analysis, and therefore were suitable candidates to improve the model representation of evaporative fluxes. Sensitivities for the streamflow performance, in turn, were mostly driven by the soil and routing parameters, with little contribution from the vegetation parameters. The multiobjective calibration experiments were carried out for the most parsimonious parameterization after a comparative analysis of the performance gains and losses for streamflow and evaporation, and yielded optimal adjustments for both hydrologic variables simultaneously. Results from this study will help to develop a better understanding of the trade-offs resulting from the joint integration of streamflow and evaporation data into modeling frameworks.ALHAMBRA cluster (http://alhambra. ugr.es) of the University of GranadaProject P20_00035, funded by the FEDER/ Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades, the project CGL2017-89836-RThe Spanish Ministry of Economy and CompetitivenessEuropean Community Funds (FEDER)The project PID2021- 126401OB-I00MCIN/ AEI/10.13039/501100011033/FEDER Una manera de hacer Europa and the project LifeWatch-2019-10-UGR-01 funded by FEDER/Ministerio de Ciencia e InnovaciónThe Ministry of Education, Culture and Sport of Spain through an FPU Grant (reference FPU17/02098)Aid for Research Stays in the Hydrology and Quantitative Water Management Group of Wageningen University (reference EST19/00169)Universidad de Granada/CBU