Design of in-building wireless networks deployments using evolutionary algorithms

Molina-García, Mariano; Calle-Sánchez, Jaime; González-Merino, Carlos; Fernandez-Duran, A.; Alonso, J.I.

doi:10.3233/ica-140474

Cited by 18 publications

(9 citation statements)

References 28 publications

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“…Darwin's work is the inspiration behind evolutionary computing and genetic algorithms used extensively in two broad areas of adaptive optimization and learning (Adeli, Hung 1995;Mesejo et al 2015) with applications in a variety of domains in the past three decades. Examples include time-cost tradeoff analysis in a construction schedule, production-distribution planning (Jia et al 2014), sustainable process planning and scheduling (Li et al 2015), automatic learning of image filters (Paris et al 2015), design optimization of counterrotating compressors (Joly et al 2014), design of wireless networks (Molina-García et al 2014), and development of a clustering algorithm (Menendez et al 2014).…”

Section: Final Remarksmentioning

confidence: 99%

Brief History of Natural Sciences for Nature-Inspired Computing in Engineering

Siddique

Adeli

2016

Journal of Civil Engineering and Management

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The goal of the authors is adroit integration of three mainstream disciplines of the natural sciences, physics, chemistry and biology to create novel problem solving paradigms. This paper presents a brief history of the development of the natural sciences and highlights some milestones which subsequently influenced many branches of science, engineering and computing as a prelude to nature-inspired computing which has captured the imagination of computing researchers in the past three decades. The idea is to summarize the massive body of knowledge in a single paper succinctly. The paper is organised into three main sections: developments in physics, developments in chemistry, and developments in biology. Examples of recently-proposed computing approaches inspired by the three branches of natural sciences are provided.

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Section: Final Remarksmentioning

confidence: 99%

Brief History of Natural Sciences for Nature-Inspired Computing in Engineering

Siddique

Adeli

2016

Journal of Civil Engineering and Management

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“…They were proposed by John Holland in the 70 s as an automatic way to provide a mechanism for parallel and adaptive search (for a solution to a problem), based on the principle of survival of the fittest (where fitness is defined by the survival of an individual i.e., a solution). A considerable amount of academic research has been published on the use of GAs and/or GA-based hybridizations in several application areas, such as array design [9,12,13], infrastructure engineering [15,58,59], civil engineering [25,30,33,64], mechanical engineering [31], aerospace engineering [5], structural engineering [60,61], urban transportation planning [29,72], image processing [11], machine learning [18,37,57], robotics [56], network design [46,55,68,71] mathematics [70], to mention a few.…”

Section: Introductionmentioning

confidence: 99%

Automatic learning of image filters using Cartesian genetic programming

Paris

Pedrino

Nicoletti

2015

ICA

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This paper proposes a computational modeling for image filtering processes based on the Cartesian Genetic Programming (CGP) methodology, suitable for hardware devices. A computational system named ALIF-CGP (Automatic Learning of Image Filters Using Cartesian Genetic Programming) was designed as a simulator for automatically constructing a sequence of operators, mainly morphological and logical, which can filter a particular shape of image. ALIF-CGP is a convenient option for executing the non-trivial task, usually manually done by human experts, of selecting the sequence of nonlinear operators to be used in morphological filters. ALIF-CGP has already a built-in pool of morphological and logical operators, which can be used by default. The user, however, has the flexibility of choosing only those operators which are of interest or then, conveniently introduce new ones. The system expects as input a pair of images (input-target). The flexibility given by the CGP-based computational modeling used by ALIF-CGP as well as its efficiency and satisfactory results, obtained in various image processing case studies, recommend its use when developing a hardware implementation for the purposes of image filtering. A few case studies using ALIF-CGP are presented and comparatively analyzed in relation to previous results available in the literature.The manual process employed by humans, when dealing with digital image processing, is usually very slow due to the trial and error approach commonly used. The ad-hoc nature of the problem invariably turns the search for the best sequence of image operators into a complex task and its solution unsuitable for reuse.Many available research works published over the past years have focused their efforts trying to reduce the complexity involved in designing new image operators that would be able to perform various computational tasks [8,9,49,50,65]. Particularly, computational tools based on the mathematical morphology formalism are considered the most effective approach when applied in practical and theoretical problems from areas such as image analysis and image processing. Developments in these areas have great importance and impact in subjacent areas such as robotic vision, visual inspection, medicine, analysis of textures, among many others.

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“…The GA and its variations have been used to solve optimization problems such as highway alignment, production distribution planning, wireless networks deployments, steel structure, cost optimization of structures, and computational intelligence (Shafahi and Bagherian, ; Jia et al., ; Molina‐García et al., ; Sarma and Adeli, ; Adeli and Sarma, ; Siddique and Adeli, ). Similarly, Dzeng et al.…”

Section: Introductionmentioning

confidence: 99%

An Activity‐Based Simulation Model for Assessing Function Space Assignment for Buildings: A Service Performance Perspective

Dzeng

Wang

Hsiao

et al. 2015

Computer aided Civil Eng

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Function space assignment determines the function of all spaces and is essential to the design of a remodeled building. Optimization of function space assignment that relies upon users' activities is difficult due to the lack of quantifiable and detailed data about users' activities. In this paper, an activity-based simulation process model consisting of five phases is proposed and applied to an actual project. The phases include activity data collection, activity simulation, space movement pattern mining, function-space assignment optimization, movement simulation and assessment. The process also allows the building administrator to adjust the optimized assignment to accommodate hard-to-quantify preferences via a variety of indexes such as movement distance, corridor flow, and other space usage-related indexes. Results from application of the proposed model to an actual remodeling project indicated that the model offered the administrator a better assignment than the contracted architect, with a 5.79% increase in the objective function and a 23.92% decrease in movement distance.

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Design of in-building wireless networks deployments using evolutionary algorithms

Cited by 18 publications

References 28 publications

Brief History of Natural Sciences for Nature-Inspired Computing in Engineering

Brief History of Natural Sciences for Nature-Inspired Computing in Engineering

Automatic learning of image filters using Cartesian genetic programming

An Activity‐Based Simulation Model for Assessing Function Space Assignment for Buildings: A Service Performance Perspective

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