An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies

2019 ◽  
Vol 233 (6) ◽  
pp. 1099-1105 ◽  
Author(s):  
Mohamed Arezki Mellal ◽  
Enrico Zio
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Strong Dependence ◽  
Integer Optimization ◽  
System Availability ◽  
Cuckoo Optimization Algorithm ◽  
Availability Constraint ◽  
System Design Optimization ◽  
Minimum System

This article presents an algorithm for optimal redundancy and repair team allocation with respect to minimum system cost and a system availability constraint. Four scenarios are considered for the failures occurring in the subsystems of the system: independence, linear dependence, weak dependence, and strong dependence. An adaptive cuckoo optimization algorithm is developed for solving the nonlinear integer optimization problem of allocation. A series–parallel system with six subsystems is considered as a case study for demonstration purposes. The results obtained highlight the good performance of the developed algorithm.

scholarly journals Multi-objective System Design Optimization via PPA and a Fuzzy Method

2021 ◽  
Author(s):  
Mohamed Arezki Mellal ◽  
Abdellah Salhi
Keyword(s):  
System Design ◽  
Cost Optimization ◽  
System Availability ◽  
Fuzzy Method ◽  
Multi Objective ◽  
Objective System ◽  
System Design Optimization ◽  
Series Systems ◽  
Numerical Case Study

AbstractSystem design deals with various challenges of targets and resources, such as reliability, availability, maintainability, cost, weight, volume, and configuration. This paper deals with the multi-objective system availability and cost optimization of parallel–series systems by resorting to the multi-objective strawberry algorithm also known as the Plant Propagation Algorithm or PPA and a fuzzy method. It is the first implementation of this optimization algorithm in the literature for this kind of problem to generate the Pareto Front. The fuzzy method allows helping the decision maker to select the best compromise solution. A numerical case study involving 10 subsystems highlights the applicability of the proposed approach.

scholarly journals Penerapan Bee Colony Optimization Algorithm untuk Penentuan Rute Terpendek (Studi Kasus : Objek Wisata Daerah Istimewa Yogyakarta)

2013 ◽  
Vol 7 (1) ◽  
pp. 65
Author(s):  
Danuri Danuri ◽  
Widodo Prijodiprodjo
Keyword(s):  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Waggle Dance ◽  
Main Process ◽  
Bee Colony ◽  
Complete Problems ◽  
Shortest Distance ◽  
Bee Colony Optimization

AbstrakPencarian rute terpendek merupakan suatu permasalahan optimasi yang sering dijadikan studi kasus bagi penelitian. Jarak merupakan faktor yang paling menentukan dalam melakukan penelusuran jalur-jalur yang akan dilalui. Jalur dengan jarak terpendek akan dipilih sebagai jalur pilihan.Algoritma bee colony optimization digunakan dalam penelitian ini untuk menyelesaikan permasalah pencarian rute terpendek. Terdapat dua proses utama pada saat penelusuran jalur yaitu forward dan backward. Algoritma bee colony optimization bekerja pada proses forward. Nilai probabilitas suatu jalur dijadikan dasar pada proses transisi jalur kemudian durasi waggle dance dari tiap lebah yang berhasil menemukan posisi tujuan akan dijadikan rute pilihan.Hasil yang diperoleh dalam penelitian ini adalah algoritma bee colony optimization dapat digunakan untuk menemukan rute terpendek. Jumlah lebah yang dilepas sangat mempengaruhi dalam menemukan rute-rute yang bisa dilalui. Semakin banyak jumlah lebah yang dilepas semakin besar peluang ditemukannya rute terpendek. Kata kunci— Rute Terpendek, Algoritma Bee Colony Optimization.  AbstractThe shortest path determination is an optimization problem which often used as a case study for research. Distance is the most defining factor in performing the search paths to be passed. Path with the shortest distance would be chosen as a path selection.Bee colony optimization algorithm used in this study to complete problems shortest path determination. There are two main process es during search path that is forward and backward. Bee colony optimization algorithm works on the process forward. The value probability of a path is base intransition process and the duration of waggle dance track of every bee who had found the position of the goal will be a preferred route.The results obtained in this study is the bee colony optimization algorithm can be used to find shortest path. The number of bees are released greatly affects in finding routes that can be passed. The more the number of bees that removed the greater the chances of finding the shortest path. Keyword— Shortest Path, Bee Colony Optimization Algorithm

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

Cuckoo optimization algorithm in reverse logistics: A network design for COVID-19 waste management

2021 ◽  
pp. 0734242X2110039
Author(s):  
Elham Shadkam
Keyword(s):  
Waste Management ◽  
Network Design ◽  
Optimization Algorithm ◽  
Reverse Logistics ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Fixed Costs ◽  
Logistics System ◽  
Cuckoo Optimization Algorithm ◽  
Proposed Model

Today, reverse logistics (RL) is one of the main activities of supply chain management that covers all physical activities associated with return products (such as collection, recovery, recycling and destruction). In this regard, the designing and proper implementation of RL, in addition to increasing the level of customer satisfaction, reduces inventory and transportation costs. In this paper, in order to minimize the costs associated with fixed costs, material flow costs, and the costs of building potential centres, a complex integer linear programming model for an integrated direct logistics and RL network design is presented. Due to the outbreak of the ongoing global coronavirus pandemic (COVID-19) at the beginning of 2020 and the consequent increase in medical waste, the need for an inverse logistics system to manage waste is strongly felt. Also, due to the worldwide vaccination in the near future, this waste will increase even more and careful management must be done in this regard. For this purpose, the proposed RL model in the field of COVID-19 waste management and especially vaccine waste has been designed. The network consists of three parts – factory, consumers’ and recycling centres – each of which has different sub-parts. Finally, the proposed model is solved using the cuckoo optimization algorithm, which is one of the newest and most powerful meta-heuristic algorithms, and the computational results are presented along with its sensitivity analysis.

Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design

2021 ◽  
Vol 63 (3) ◽  
pp. 266-271
Author(s):  
Hammoudi Abderazek ◽  
Ferhat Hamza ◽  
Ali Riza Yildiz ◽  
Sadiq M. Sait
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Spur Gear ◽  
Comparative Investigation ◽  
Whale Optimization Algorithm ◽  
Solution Quality ◽  
Gear Design ◽  
Geometric Conditions ◽  
Swarm Algorithms ◽  
Whale Optimization

Abstract In this study, two recent algorithms, the whale optimization algorithm and moth-flame optimization, are used to optimize spur gear design. The objective function is the minimization of the total weight of the spur gear pair. Moreover, the optimization problem is subjected to constraints on the main kinematic and geometric conditions as well as to the resistance of the material of the gear system. The comparison between moth-flame optimization (MFO), the whale optimization algorithm (WOA), and previous studies indicate that the final results obtained from both algorithms lead to a reduction in gear weight by 1.05 %. MFO and the WOA are compared with four additional swarm algorithms. The experimental results indicate that the algorithms introduced here, in particular MFO, outperform the four other methods when compared in terms of solution quality, robustness, and high success rate.

Sign in / Sign up

Export Citation Format

Share Document