A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

[EN] The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).García, J.; Yepes, V.; Martí Albiñana, JV. (2020). A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem. Mathematics. 8(4):1-22. https://doi.org/10.3390/math8040555S12284García, J., Altimiras, F., Peña, A., Astorga, G., & Peredo, O. (2018). A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems. Complexity, 2018, 1-15. doi:10.1155/2018/8395193García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., … Astorga, G. (2019). 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Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Binary Cuckoo Search Untuk Optimasi Portofolio Dengan Kendala Cardinality

Cuckoo Search merupakan salah satu metode optimasi metaheuristik yang terinspirasi dari tingkah laku burung Cuckoo. Algoritma Cuckoo Search pertama kali dikembangkan oleh Yang dan Deb pada tahun 2009. Pada dasarnya algoritma ini dikembangkan untuk menyelesaikan masalah optimasi yang hanya melibatkan variabel kontinu (real). Akan tetapi, pada Kenyataannya banyak masalah optimasi yang melibatkan tidak hanya variabel real, teapi juga variabel diskrit, bulat, dan biner. Masalah optimasi portofolio dengan kendala cardinality, yaitu permasalahan membentuk komposisi dari berbagai aset dalam portofolio sehingga didapatkan hasil yang optimal dengan cara memilih aset dari aset yang tersedia. Masalah optimasi portofolio dengan kendala cardinality melibatkan variabel real dan biner (0-1). Pada variabel biner, nol artinya aset tersebut tidak dipilih sedangkan satu artinya aset tersebut dipilih untuk dimasukkan ke dalam portofolio. Pada penelitian ini penulis memodifikasi algoritma Cuckoo Search sehingga mampu mengatasi masalah optimasi yang melibatkan variabel real dan biner, yang selanjutnya disebut algoritma Binary Cuckoo Search. Kemudian algoritma tersebut diterapkan pada masalah meminimumkan risiko (Minrisk) dan memaksimumkan return (Maxret) dengan memilih 20 saham dari 31 saham Hangseng. Hasil penelitian menunjukkan bahwa Binary Cuckoo Search merupakan algoritma yang handal untuk mengatasi masalah optimasi portofolio dengan kendala cardinality

The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm

[EN] The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10^20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).Garcia, J.; Martí Albiñana, JV.; Yepes, V. (2020). The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm. Mathematics. 8(6):862-01-862-22. https://doi.org/10.3390/math8060862S862-01862-228

BINARY CUCKOO SEARCH UNTUK OPTIMASI PORTOFOLIO DENGAN KENDALA CARDINALITY

Cuckoo Search merupakan salah satu metode optimasi metaheuristik yang terinspirasi dari tingkah laku burung Cuckoo. Algoritma Cuckoo Search pertama kali dikembangkan oleh Yang dan  Deb pada tahun 2009. Pada dasarnya algoritma ini dikembangkan untuk menyelesaikan masalah optimasi yang hanya melibatkan variabel kontinu (real). Akan tetapi, pada kenyataannya banyak masalah optimasi yang melibatkan tidak hanya variabel real, teapi juga variabel diskrit, bulat, dan biner. Masalah optimasi portofolio dengan kendala cardinality, yaitu permasalahan membentuk komposisi dari berbagai aset dalam portofolio sehingga didapatkan hasil yang optimal dengan cara memilih  aset dari  aset yang tersedia. Masalah optimasi portofolio dengan kendala cardinality melibatkan variabel real dan biner (0-1). Pada variabel biner, nol artinya aset tersebut tidak dipilih sedangkan satu artinya aset tersebut dipilih untuk dimasukkan ke dalam portofolio. Pada penelitian ini penulis memodifikasi algoritma Cuckoo Search sehingga mampu mengatasi masalah optimasi yang melibatkan variabel real dan biner, yang selanjutnya disebut algoritma Binary Cuckoo Search. Kemudian algoritma tersebut diterapkan pada masalah meminimumkan risiko (Minrisk) dan memaksimumkan return (Maxret) dengan memilih 20 saham dari 31 saham Hangseng. Hasil penelitian menunjukkan bahwa Binary Cuckoo Search merupakan algoritma yang handal untuk mengatasi masalah optimasi portofolio dengan kendala cardinality. Kata Kunci: algoritma cockoosearch, binary cuckoo search, optimasi portofolio, cardinalit

Ingenious Method for Conducive Handoff Appliance in Cognitive Radio Networks

Wireless communications deployed in the current epoch claims ceaseless connection among its users thereby leading to the investigation of Cognitive Radio Networks (CRN) which enables to make use of unallocated spectrum optimally and provides uninterrupted connection. Establishing interminable connectivity during the handoff process in spectrum mobility of CRN is a challenging task. This paper elucidates the optimization of handoff process carried out in CRN by incorporating an intelligent method. This includes fuzzy logic wherein the handoff parameters are processed thereby indicating the need of handoff. The proffered method also comprises of a part of genetic algorithm which yields fitness value for reducing the handoff occurrences and enhancing the overall performance of the system is promoted using cuckoo search which decides the mobile node from which the handoff process has to initiate based on the priority generated. This technique ensures that decision is taken ahead of link failure rather than range failure which are the key point in comparison to the existing system. Results obtained through the simulation are satisfactory in terms of delay, throughput, number of failed handoff and handoffs performed in comparison to the existing fuzzy based handoff process in CRN