Parameter Optimization of Genetic Algorithm Utilizing Taguchi Design for Gliding Trajectory Optimization of Missile

The present study aims to establish a genetic algorithm (GA) method to optimize gliding trajectory of a missile. The trajectory is optimized by discretizing the angle of attack (AOA) and solving optimal control problem to achieve maximum gliding range. GA is employed to resolve the optimal control problem to achieve optimized AOA. A Taguchi’s design of experiments was proposed contrary to full factorial method to ascertain the GA parameters. The experiments have been designed as per Taguchi’s design of experiments using L27 orthogonal array. Systematic reasoning ability of Taguchi method is exploited to obtain better selection, crossover and mutation operations and consequently, enhance the performance of GA for gliding trajectory optimization. The effects of GA parameters on gliding trajectory optimization are studied and analysis of variance (ANOVA) is carried out to evaluate significance factors on the results. Crossover function and population size are observed as highly impacting parameter in missile trajectory optimization accompanied by selection method, crossover fraction, mutation rate and number of generations. Artificial neural network (ANN) method was also applied to predict the significance of GA parameters. The results show that the gliding range is maximized after GA parameter tuning. Simulation results also portrayed that with optimal AOA, gliding distance of missile is improved compared to earlier one. The numerical simulation shows the efficiency of proposed procedure via various test scenarios

Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations

This article presents the use of the equations of the dynamic response to a step input in metaheuristic algorithm for the parametric estimation of a motor model. The model equations are analyzed, and the relations in steady-state and transient-state are used as delimiters in the search. These relations reduce the number of random parameters in algorithm search and reduce the iterations to find an acceptable result. The tests were implemented in two motors of known parameters to estimate the performance of the modifications in the algorithms. Tests were carried out with three algorithms (Gray Wolf Optimizer, Jaya Algorithm, and Cuckoo Search Algorithm) to prove that the benefits can be extended to various metaheuristics. The search parameters were also varied, and tests were developed with different iterations and populations. The results show an improvement for all the algorithms used, achieving the same error as the original method but with 10 to 50% fewer iterationsThis research received no external funding. Partial funding for open access charge: Universidad de Málag

A Study About Meta-Optimizing the NSGA-II Multi-Objective Evolutionary Algorithm.

The automatic design of multi-objective metaheuristics is an active research line aimed at, given a set of problems used as training set, to find the configuration of a multi-objective optimizer able of solving them efficiently. The expected outcome is that the auto-configured algorithm can be used of find accurate Pareto front approximations for other problems. In this paper, we conduct a study on the meta-optimization of the wellknown NSGA-II algorithm, i.e., we intend to use NSGA-II as an automatic configuration tool to find configurations of NSGA-II. This search can be formulated as a multi-objective problem where the decision variables are the NSGA-II components and parameters and the the objectives are quality indicators that have to be minimized. To develop this study, we rely on the jMetal framework. The analysis we propose is aimed at answering the following research questions: RQ1 - how complex is to build the meta-optimization package?, and RQ2 - can accurate configurations be found? We conduct an experimentation to give an answer to these questions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Perakende Sektöründe Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Performans Analizi: Black Friday Satış Tahminlemesi

Büyük perakende zincirlerinin şube ağlarının genişlemesi, müşteri tabanlarının büyümesi ve artan müşteri profili heterojenliği satış tahminleme süreçlerinin karmaşıklığını artırmaktadır. Müşteri çeşitliliği ve bu çeşitliliğin yönetilmesi, perakendeciler için hem stratejik planlama hem de operasyonel düzeyde uygulama açısından önemli bir güçlük oluşturmaktadır. Bu noktada, müşteri segmentasyonu ve kişiselleştirilmiş pazarlama stratejileri geliştirmek, her bir müşteri grubuna özel yaklaşımlar belirlemek ve bu çeşitliliği anlayarak etkili bir şekilde yönetmek önem kazanmaktadır. Gelişen teknolojiler, özellikle makine öğrenmesi yöntemleri söz konusu zorluklarla başa çıkma potansiyeli sunmaktadır. Bu kapsamda araştırmanın amacı, bir perakende firmasının Black Friday günündeki satış veri seti üzerinde Doğrusal Regresyon, Rastgele Orman Regresyonu, K-En Yakın Komşu Regresyonu, XGBoost Regresyonu, Karar Ağacı Regresyonu ve LGBM Regresyonu isimli makine öğrenmesi algoritmaları aracılığıyla satış tahminlemesi gerçekleştirmek ve algoritmaların performanslarını karşılaştırarak en iyi performans gösteren algoritmayı belirlemektir. Ayrıca, GridSearchCV kullanarak hiperparametrelerin ayarlanması ve bu ayarlamaların modellerin performanslarına etkisinin incelenmesi amaçlanmaktadır. Buna ek olarak, veri seti üzerinde Keşifsel Veri Analizleri yürütülerek, perakende sektöründeki işletmelerin ellerinde bulunan verilerden ne tür bilgiler çıkarabileceklerine ve bu bilgileri nasıl değerlendirebileceklerine ilişkin bir örnek oluşturmak araştırmanın diğer önemli bir amacıdır. Araştırmadan elde edilen sonuçlara göre, satışları tahminlemede en başarılı algoritma GridSearchCV ile hiperparametreleri ayarlanmış XGBoost Regresyonu olmuştur. Firma müşterilerinin en çok 26-35 yaş aralığında bireylerden oluştuğu, erkek müşterilerin kadınlara, bekar müşterilerin evlilere göre önemli ölçüde daha yüksek tutarlı alışverişler yaptığı saptanmıştır. Ayrıca, satın alım tutarı ortalaması bağlamında bakıldığında en yüksek harcama ortalamasına sahip yaş grubu 51-55 yaş aralığı olarak tespit edilmiştir

Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives

In recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area.Javier Del Ser acknowledges funding support from the Basque Government (consolidated research group MATHMODE, Ref. IT1294-19). Antonio J. Nebro is supported by the Spanish Ministry of Science and Innovation via Grant PID2020-112540RB-C41 (AEI/FEDER, UE) and the Andalusian PAIDI program with Grant P18-RT-2799

Review, challenges, design, and development

Peres, F., & Castelli, M. (2021). Combinatorial optimization problems and metaheuristics: Review, challenges, design, and development. Applied Sciences (Switzerland), 11(14), 1-39. [6449]. https://doi.org/10.3390/app11146449In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.publishersversionpublishe

A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

Population clustering methods, which consider the position and fitness of the individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method