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An accelerated PSO for query expansion in web information retrieval: application to medical dataset

APPLIED intelligence 2017年第3期47卷 793-808页

作者： Khennak, Ilyes Drias, Habiba USTHB Lab Res Artificial Intelligence Comp Sci Dept BP 32 El Alia 16111 Algiers Algeria

swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web's largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

关键词： Information retrieval Web intelligence Query expansion swarm intelligence algorithms Accelerated particle swarm optimization MEDLINE

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Comparison of swarm intelligence Methods for Suppressing PAPR in CO-OFDM Systems 15

Comparison of Swarm Intelligence Methods for Suppressing PAP...

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15th International Conference on Optical Communications and Networks (ICOCN)

作者： Wang, Shaoying Liu, Jianfei Yi, Ziyao Zeng, Xiangye Lu, Jia Wang, Mengjun Hebei Univ Technol Sch Elect & Informat Engn Key Lab Elect Mat & Devices Tianjin Tianjin Peoples R China

ISBN: (纸本)9781509034918

swarm intelligence algorithms are applied to suppressing peak to average power ratio (PAPR) in CO-OFDM system by optimizing phase of subcarriers. The result shows that all of these algorithms can effectively reduce PAPR, and the PAPR of CSO optimized signal is reduced by 1.5dB, 0.77dB and 0.28dB compared with that of PSO, BSA and BA optimized signals.

关键词： PAPR CO-OFDM Number of population swarm intelligence algorithms

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swarm intelligence-based Power Allocation and Relay Selection Algorithm for wireless cooperative network

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2016年第3期10卷 1111-1130页

作者： Xing, Yaxin Chen, Yueyun Lv, Chen Gong, Zheng Xu, Ling Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle swarm Ant Colony Optimization) based on PSACO (Particle swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

关键词： Wireless cooperative communication Power allocation Relay selection swarm intelligence algorithms PSACO

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A comprehensive review: Krill Herd algorithm (KH) and its applications

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APPLIED SOFT COMPUTING 2016年 49卷 437-446页

作者： Bolaji, Asaju La'aro Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Khader, Ahamad Tajudin Abualigah, Laith Mohammad Fed Univ Wukari Dept Comp Sci Fac Pure & Appl Sci PMB 1020 Wukari Taraba State Nigeria Al Balqa Appl Univ Dept Informat Technol Al Huson Univ Coll POB 50 Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Israel Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Krill Herd (KH) algorithm is a class of nature-inspired algorithm, which simulates the herding behavior of krill individuals. It has been successfully utilized to tackle many optimization problems in different domains and found to be very efficient. As a result, the studies has expanded significantly in the last 3 years. This paper presents the extensive (not exhaustive) review of KH algorithm in the area of applications, modifications, and hybridizations across these fields. The description of how KH algorithm was used in the approaches for solving these kinds of problems and further research directions are also discussed. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Krill Herd algorithm swarm intelligence algorithms Nature-inspired algorithms Metaheuristics

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Antenna design using animal migration optimisation algorithm

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JOURNAL OF ENGINEERING-JOE 2016年第8期2016卷 298-301页

作者： Ulker, Ezgi Deniz Ulker, Sadik European Univ Lefke Dept Comp Engn TR-10 Lefke Mersin Turkey European Univ Lefke Dept Elect & Elect Engn TR-10 Lefke Mersin Turkey

The use of swarm intelligence algorithms for solving complex optimisation problems is an interesting area since they can produce remarkable results. The animal migration optimisation (AMO) algorithm is one of the swarm intelligence algorithms which is derived from the migration behaviour of animals. The implementation of AMO algorithm for complex array optimisation problems is given. The experimental studies show that the AMO algorithm produces high-quality results and it is capable to overcome strong local optimum points by using its attributes. Also, it has few number of steps which are quite easy to implement. The AMO can be suggested as a powerful algorithm for optimisation problems including complex array designs when the high solution quality is desired.

关键词： design engineering swarm intelligence dipole antennas antenna feeds optimisation variable length centre fed dipole AMO algorithm migration behaviour complex array optimisation problems swarm intelligence algorithms animal migration optimisation algorithm antenna design

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Performance Comparison of Cloudlet Scheduling Policies 1

Performance Comparison of Cloudlet Scheduling Policies

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1st International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)

作者： Geetha, V. Devi, R. Aruna Ilavenil, T. Begum, S. Marjana Revathi, S. Pondicherry Engn Coll Dept Informat Technol Pondicherry India

ISBN: (纸本)9781467367257

Cloud computing is widely accepted as the best computing and storage model for low cost and high resource utilization. User requests from public can be run in data centers of large datacenters whey they are idle. This brings in high profit for cloud providers and low cost for cloud users. The profit and response time can be improved by an optimal scheduling policy. This paper does a survey on various classes of existing cloudlet scheduling algorithms. Researchers have proposed linear algorithms, genetic algorithms and swarm intelligence algorithms. The merits and demerits of these classes of algorithms are analyzed in this paper.

关键词： Cloud Virtual Machine Cloudlet cloudlet Scheduling policies swarm intelligence algorithms Linear algorithms Genetic algorithms

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Chaotic Adaptive Fireworks Algorithm

Chaotic Adaptive Fireworks Algorithm

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7th International Conference on swarm intelligence (ICSI)

作者： Gong, Chibing Guangdong Tech Coll Water Resource & Elect Engn Dept Comp Informat Engn Guangzhou 510635 Guangdong Peoples R China

ISBN: (纸本)9783319410005;9783319409993

Fireworks algorithm (FWA) is considered a novel algorithm that reacts the fireworks explosion process. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes in regards to enhancing performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add chaos to the AFWA with the goal of boosting performance and achieving global optimization. The parameter. is tuned using ten chaotic maps, and twelve benchmark functions will be tested in regards to chaotic adaptive fireworks algorithm (CAFWA). The final results conclude the CAFWA is able to outperform the FWA, EFWA, and AFWA. Additionally, the CAFWA is compared with the bat algorithm (BA), standard particle swarm optimization 2011 (SPSO2011), harmony search (HS), and firefly algorithm (FA). The research results indicated that the highest performance presented itself when CAFWA is used with Circle maps.

关键词： Chaotic maps Fireworks algorithm swarm intelligence algorithms

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A New Voting Approach Based on False Candidates 25

A New Voting Approach Based on False Candidates

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25th International-Business-Information-Management-Association Conference

作者： Mogos, Andrei-Horia Mogos, Bianca Florea, Adina Magda Univ Politehn Bucuresti Bucharest Romania Univ Bucharest Bucharest Romania

ISBN: (纸本)9780986041945

In the last decades, social choice theory had a significant impact over social sciences, political sciences, and economic sciences. Recently, a new research area, computational social choice, which brings together social choice theory and computer science, gained an important popularity. An important topic in social choice theory is the theory of voting which has a lot of applications in computer science. In this paper we propose a new voting approach based on false candidates: first, we define a new type of voting scheme (i.e. the preferences of the voters over the set of candidates) called Q - voting scheme and then we propose variants for Borda Count, Condorcet and Black's Rule voting methods that can be used for Q - voting schemes. We also introduce a formal approach and propose and prove three theorems related to the analyzed subject. In addition, we use Q - voting schemes for comparing three swarm intelligence algorithms;during the comparison process we apply an algorithm, also proposed in this paper, which automatically generates Q - voting schemes.

关键词： voting methods false candidates Borda Count Condorcet Black's Rule swarm intelligence algorithms

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Computational Models & Methods in Systems Biology & Medicine

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IET SYSTEMS BIOLOGY 2015年第6期9卷 217-217页

作者： Ruskin, Heather J. Roznovat, Irina A.

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A hybrid artificial bee colony algorithm for numerical function optimization

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2015年第10期26卷 1550109-1550109页

作者： Alqattan, Zakaria N. Abdullah, Rosni Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms;it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

关键词： swarm intelligence algorithms artificial bee colony algorithm particle swarm optimization numerical function optimization

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