An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm

AbstractThe main goal of the present paper is to present a penalty based cuckoo search (CS) algorithm to get the optimal solution of reliability – redundancy allocation problems (RRAP) with nonlinear resource constraints. The reliability – redundancy allocation problem involves the selection of components' reliability in each subsystem and the corresponding redundancy levels that produce maximum benefits subject to the system's cost, weight, volume and reliability constraints. Numerical results of five benchmark problems are reported and compared. It has been shown that the solutions by the proposed approach are all superior to the best solutions obtained by the typical approaches in the literature are shown to be statistically significant by means of unpaired pooled t-test

Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks

The present work deals with the application of coevolutionary algorithms and artificial neural networks to perform input selection and related parameter estimation for nonlinear black-box models in system identification. In order to decouple the resolution of the input selection and parameter estimation, we propose a problem decomposition formulation and solve it by a coevolutionary algorithm strategy. The novel methodology is successfully applied to identify a magnetorheological damper, a continuous polymerization reactor and a piezoelectric robotic micromanipulator. The results show that the method provides valid models in terms of accuracy and statistical properties. The main advantage of the method is the joint input and parameter estimation, towards automating a tedious and error prone procedure with global optimization algorithms

QoS multicast routing protocol oriented to cognitive network using competitive coevolutionary algorithm

The human intervention in the network management and maintenance should be reduced to alleviate the ever-increasing spatial and temporal complexity. By mimicking the cognitive behaviors of human being, the cognitive network improves the scalability, self-adaptation, self-organization, and self-protection in the network. To implement the cognitive network, the cognitive behaviors for the network nodes need to be carefully designed. Quality of service (QoS) multicast is an important network problem. Therefore, it is appealing to develop an effective QoS multicast routing protocol oriented to cognitive network. In this paper, we design the cognitive behaviors summarized in the cognitive science for the network nodes. Based on the cognitive behaviors, we propose a QoS multicast routing protocol oriented to cognitive network, named as CogMRT. It is a distributed protocol where each node only maintains local information. The routing search is in a hop by hop way. Inspired by the small-world phenomenon, the cognitive behaviors help to accumulate the experiential route information. Since the QoS multicast routing is a typical combinatorial optimization problem and it is proved to be NP-Complete, we have applied the competitive coevolutionary algorithm (CCA) for the multicast tree construction. The CCA adopts novel encoding method and genetic operations which leverage the characteristics of the problem. We implement and evaluate CogMRT and other two promising alternative protocols in NS2 platform. The results show that CogMRT has remarkable advantages over the counterpart traditional protocols by exploiting the cognitive favors

Imperialist Competitive Algorithm with Independence and Constrained Assimilation

Autonomous Supply Chai

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Differential Evolution in Wireless Communications: A Review

Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this contex

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Analysis of nature inspired algorithms and their application in electrical power engineering

Ker so viri, čas in denar v resničnem svetu vedno omejeni, moramo najti rešitve za optimalno porabo teh pomembnih virov. Za reševanje večine optimizacijskih problemov resničnega sveta potrebujemo mnogokrat zapleteno optimizacijsko orodje. Na naravi osnovani meta-hevristični algoritmi so eni izmed najpogosteje uporabljenih algoritmov za optimizacijo. Algoritem kresničk je eden od teh algoritmov. V tem delu so analizirani optimizacijski algoritmi od tradicionalnih metod do modernih meta-hevrističnih algoritmov, s poudarkom na algoritmih osnovanih na naravi. To delo poskuša predstaviti zgodovino in aplikacijo teh algoritmov. Prvo poglavje predstavi algoritme in analizira bistvo algoritma. Potem se razpravlja osnovno oblikovanje optimizacijskega problema in moderne pristope s pogleda inteligence rojev. Pregledana je kratka zgodovina na naravi osnovanih algoritmov. Drugo poglavje analizira ključne komponente na naravi osnovanih algoritmov s pogleda njihovih evolucijskih operatorjev in funkcionalnosti. Glavni cilj je podati pregled teh algoritmov. V tretjem poglavju se predstavi standardni algoritem kresničk in potem so na kratko predstavljene različice. Analizirane so tudi karakteristike algoritma kresničk. Četrto poglavje predstavi implementacijo algoritma kresničk pri reševanju problema optimalne razporeditve obratovanja elektrarn z minimiziranjem stroškov goriva in upošteva omejitve generatorjev in izgube prenosa. Temu sledi kratek pregled na naravi osnovanih algoritmov v elektroenergetskih sistemih.Because resources, time and money are always limited in real world applications, we have to find solutions to optimally use these valuable resources. To solve most real world optimization problems we need sophisticated optimization tools. Nature inspired meta-heuristic algorithms are among the most widely used algorithms for optimization. Firefly algorithm is one of these algorithms. In this work optimization algorithms are analyzed from traditional methods to modern meta-heuristic algorithms, with an emphasis on nature inspired algorithms. This work is attempts to present the history and applications of these algorithms. The first chapter introduces algorithms and analyzes the essence of the algorithm. Then the general formulation of an optimization problem is discussed and modern approaches in terms of swarm intelligence. A brief history of nature inspired algorithms is reviewed. The second chapter analyzes the key components nature inspired algorithms in terms of their evolutionary operators and functionalities. The main aim is to provide an overview of these algorithms. In the third chapter the standard firefly algorithm is introduced and then the variants are briefly reviewed. The characteristics of firefly algorithm are also analyzed. The forth chapter presents the implementation of firefly algorithm in solving the economic dispatch problem by minimizing the fuel cost and considering the generator limits and transmission losses. This is followed by a short review of applications nature inspired algorithms in power systems

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp