Backtracking Search Optimization for Collaborative Beamforming in Wireless Sensor Networks

Due to energy limitation and constraint in communication capabilities, the undesirable high battery power consumption has become one of the major issues in wireless sensor network (WSN). Therefore, a collaborative beamforming (CB) method was introduced with the aim to improve the radiation beampattern in order to compensate the power consumption. A CB is a technique which can increase the sensor node gain and performance by aiming at the desired objectives through intelligent capabilities. The sensor nodes were located randomly in WSN environment. The nodes were designed to cooperate among each other and act as a collaborative antenna array. The configuration of the collaborative nodes was modeled in circular array formation. The position of array nodes was determined by obtaining the optimum parameters pertaining to the antenna array which implemented by using Backtracking Search Optimization Algorithm (BSA). The parameter considered in the project was the side-lobe level minimization. It was observed that, the suppression of side-lobe level for BSA was better compared to the radiation beampattern obtained for conventional uniform circular array

GPU parallelization strategies for metaheuristics: a survey

Metaheuristics have been showing interesting results in solving hard optimization problems. However, they become limited in terms of effectiveness and runtime for high dimensional problems. Thanks to the independency of metaheuristics components, parallel computing appears as an attractive choice to reduce the execution time and to improve solution quality. By exploiting the increasing performance and programability of graphics processing units (GPUs) to this aim, GPU-based parallel metaheuristics have been implemented using different designs. RecentresultsinthisareashowthatGPUstendtobeeffectiveco-processors forleveraging complex optimization problems.In thissurvey, mechanisms involvedinGPUprogrammingforimplementingparallelmetaheuristicsare presentedanddiscussedthroughastudyofrelevantresearchpapers. Metaheuristics can obtain satisfying results when solving optimization problems in a reasonable time. However, they suffer from the lack of scalability. Metaheuristics become limited ahead complex highdimensional optimization problems. To overcome this limitation, GPU based parallel computing appears as a strong alternative. Thanks to GPUs, parallelmetaheuristicsachievedbetterresultsintermsofcomputation,and evensolutionquality

Hybrid differential evolution algorithms for the optimal camera placement problem

Purpose – This paper investigates to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem. Design/methodology/approach – This problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, a greedy algorithm, and a row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera in order to find better solutions. Findings – The experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice. Originality/value – Up to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations in order to fully benefit from the DE mutation scheme

Swarm Intelligence Based Optimization: First International Conference, ICSIBO 2014, Mulhouse, France, May 13-14, 2014. Revised Selected Papers

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Swarm Intelligence Based Optimization: Second International Conference, ICSIBO 2016, Mulhouse, France, June 13-14, 2016. Revised Selected Papers

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A* based differential evolution algorithm for UAV path planning problem in urban environment

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MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS

International audienceAbstract. This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 × 256 pixels for the Sentinel-2 L2A , Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Use/Land Cover (LULC) topographic reference database. With MultiSenGE, we contribute to the recents developments towards shared data use and machine learning methods in the field of environmental science. The purpose of this dataset is to propose relevant and easy-access dataset to explore deep learning methods. We use MultiSenGE to evaluate the performance for urban areas using well-known deep learning techniques. These results serve as a baseline for future research on remote sensing applications using the multi-temporal and multimodal aspects of MultiSenGE. With all patches georeferenced at a 10 meters spatial resolution covering the whole Grand-Est Region, MultiSenGE provides an opportunity for environmental benchmark dataset will help to advance data-driven techniques for land use/land cover remote sensing applications

An improved TLBO algorithm for solving UAV path planning problem

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Hybrid backtracking search optimization algorithm and K-means for clustering in wireless sensor networks

Rapid technology evolvement in the area of wireless sensor networks (WSNs) has led to many application-specific protocols that are particularly developed to cover different fields of usage and various network scenarios. Energy efficiency is one of the apparent challenges facing WSNs which has impacted immensely on the network performance. Hence, clustering protocols that eliminate energy inefficiencies in the network is essential. As finding an optimal set of cluster heads is an NP-hard problem, the application of heuristic algorithm is required to produce good clustering. In this paper, we propose a clustering solution for WSNs using a hybrid algorithm based on Backtracking Search Optimization Algorithm (BSA) and K-Means. A fitness function that incorporates aspects such as expected energy consumption in the network and maximum intra-cluster distance is utilized to address the problem of energy efficiency. Performance comparison against well-known clustering protocols such as LEACH and LEACH-C reveals that the hybrid of BSA and K-Means clustering algorithm is able to deliver more data to the base station and extends the network lifetime