Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COP

Blended Biogeography-based Optimization for Constrained Optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems

Global and local surrogate-assisted differential evolution for expensive constrained optimization

The file attached to this record is the author's final peer reviewed version.For expensive constrained optimization problems, the computation of objective function and constraints is very time-consuming. This paper proposes a novel global and local surrogate-assisted differential evolution for solving expensive constrained optimization problems with inequality constraints. The proposed method consists of two main phases: global surrogate-assisted phase and local surrogate-assisted phase. In the global surrogate-assisted phase, differential evolution serves as the search engine to produce multiple trial vectors. Afterward, the generalized regression neural network is used to evaluate these trial vectors. In order to select the best candidate from these trial vectors, two rules are combined. The first is the feasibility rule, which at first guides the population toward the feasible region, and then toward the optimal solution. In addition, the second rule puts more emphasis on the solution with the highest predicted uncertainty, and thus alleviates the inaccuracy of the surrogates. In the local surrogate-assisted phase, the interior point method coupled with radial basis function is utilized to refine each individual in the population. During the evolution, the global surrogate-assisted phase has the capability to promptly locate the promising region and the local surrogate-assisted phase is able to speed up the convergence. Therefore, by combining these two important elements, the number of fitness evaluations can be reduced remarkably. The proposed method has been tested on numerous benchmark test functions from three test suites and two real-world cases. The experimental results demonstrate that the performance of the proposed method is better than that of other state-of-the-art methods

Dağıtım transformatörlerinin metasezgisel algoritmalarla tasarım optimizasyonu

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Dünyadaki pek çok ekonomi, yüksek verimli transformatörlerin kullanımını zorunlu kılan veya teşvik eden enerji verimliliği yönetmelikleri veya teşvik programları kabul etmiştir. Öte yandan, transformatör verimliliğindeki artışlar, transformatör ağırlık ve boyutunda bazen % 50 hatta daha fazla bir artışı gerektirmektedir. Transformatör endüstrisi bu nedenle gerçekten en iyi tasarımları geliştirme uğraşısı ile karşı karşıyadır. Transformatör tasarım optimizasyonu (TDO) problemi, karmaşık ve süreksiz amaç fonksiyonlu ve kısıtlı karma-tamsayılı bir doğrusal olmayan programlama problemidir. TDO'nun amacı, ulusal ve/veya ulusal standartlar ve müşteri şartnameleri uyarıca, mevcut malzemeleri ekonomik olarak kullanarak daha düşük boyut, ağırlık ve maliyet ve daha yüksek işletme performansı elde etmek üzere transformatörün tüm bileşenlerinin niteliklerinin detaylı olarak hesaplanmasıdır. Bu çalışmada TDO probleminin çözümü için beş modern metasezgisel optimizasyon algoritması uygulamasının ayrıntılı karşılaştırmalı analizi üç test vakası üzerinde gösterilmiş ve iki algoritma önerilmiştir önerilen bu algoritmaların, rassal özelliklerine rağmen, garanti edilmiş küresel yakınsama özelliklerine sahip oldukları doğrulanmıştır. Algoritmaların karşılaştırılması için pragmatik bir kıyaslama yöntemi geliştirilmiştir. Literatürde sunulan TDO yöntemleri nadiren üretimde doğrudan uygulanabilir çözümler üretir. tasarım mühendisinin genellikle teorik çözümü pratik olarak uygulanabilir bir hale dönüştürmek için ek çaba harcaması gerekir. Bu problem bu çalışmada ele alınmış ve piyasada mevcut veya üretime uygun boyutlara sahip çözümler üreten bir ayrık transformatör tasarım optimizasyon yöntemi önerilmiştir Ayrıca, amaç fonksiyonu ve kısıt hesaplamalarını azaltmak için basit bir yöntem önerilmiştir. Yöntem, önbellekleme tekniği kullanılarak arama işlemi sırasında yinelenen tasarım vektörleri için hesaplamaların atlanması esasına dayanmaktadır. Performans testleri, teorik TDO için Rekabetçi-Uyarlamalı Diferansiyel Gelişim ve Guguk Kuşu Arama, Pratik TDO için de Guguk Kuşu Arama ve Çiçek Tozlaşma algoritmaları kullanıldığında küresel optimum ve ona çok yakın sonuçlar elde edildiğini göstermiştir.Many economies in the world have adopted energy-efficiency requirements or incentive programs mandating or promoting the use of energy-efficient transformers. On the other hand, increases in transformer efficiency are subject to increases in transformer weight and size, sometimes as much as 50% or even more. The transformer manufacturing industry is therefore faced with the challenge to develop truly optimum designs. Transformer design optimization (TDO) is a mixed-integer nonlinear programming problem having complex and discontinuous objective function and constraints, with the objective of detailed calculation of the characteristics of a transformer based on national and/or international standards and transformer user and two algorithms are proposed, for which it has been verified that they possess guaranteed global convergence properties in spite of their inherent stochastic nature. A pragmatic benchmarking scheme is used for comparison of the algorithms. Transformer design optimization methods presented in the literature rarely yield solutions directly applicable in productionrequirements, using available materials and manufacturing processes, to minimize manufacturing cost or total owning cost, while maximizing operating performance. Detailed comparative analysis of the application of five modern metaheuristic optimization algorithms for the solution of TDO problem are carried out in this study, demonstrated on three test cases the design engineer usually needs to convert the theoretical solution to a practical one. This problem is addressed in this study, and a discrete transformer design optimization method is proposed which yields solutions with commercially available or productionally feasible dimensions Furthermore, a simple method is proposed to reduce the number of objective function and constraint calculations. The method is based on skipping calculations for design vectors recurring during the search process, by the use of caching technique Performance tests showed that global or near-global optimum solutions can be obtained with b6e6rl and CS for TDO, and CS and FPA algorithms for DTDO

Multiobjective particle swarm optimization: Integration of dynamic population and multiple-swarm concepts and constraint handling

Scope and Method of Study: Over the years, most multiobjective particle swarm optimization (MOPSO) algorithms are developed to effectively and efficiently solve unconstrained multiobjective optimization problems (MOPs). However, in the real world application, many optimization problems involve a set of constraints (functions). In this study, the first research goal is to develop state-of-the-art MOPSOs that incorporated the dynamic population size and multipleswarm concepts to exploit possible improvement in efficiency and performance of existing MOPSOs in solving the unconstrained MOPs. The proposed MOPSOs are designed in two different perspectives: 1) dynamic population size of multiple-swarm MOPSO (DMOPSO) integrates the dynamic swarm population size with a fixed number of swarms and other strategies to support the concepts; and 2) dynamic multiple swarms in multiobjective particle swarm optimization (DSMOPSO), dynamic swarm strategy is incorporated wherein the number of swarms with a fixed swarm size is dynamically adjusted during the search process. The second research goal is to develop a MOPSO with design elements that utilize the PSO's key mechanisms to effectively solve for constrained multiobjective optimization problems (CMOPs).Findings and Conclusions: DMOPSO shows competitive to selected MOPSOs in producing well approximated Pareto front with improved diversity and convergence, as well as able to contribute reduced computational cost while DSMOPSO shows competitive results in producing well extended, uniformly distributed, and near optimum Pareto fronts, with reduced computational cost for some selected benchmark functions. Sensitivity analysis is conducted to study the impact of the tuning parameters on the performance of DSMOPSO and to provide recommendation on parameter settings. For the proposed constrained MOPSO, simulation results indicate that it is highly competitive in solving the constrained benchmark problems