The community detection in dynamic networks is essential for important applications such as social network analysis. Such detection requires simultaneous maximization of the clustering accuracy at ...the current time step while minimization of the clustering drift between two successive time steps. In most situations, such objectives are often in conflict with each other. This article proposes the concept of consensus community. Knowledge from the previous step is obtained by extracting the intrapopulation consensus communities from the optimal population of the previous step. Subsequently, the intrapopulation consensus communities of the previous step obtained is voted by the population of the current time step during the evolutionary process. A subset of the consensus communities, which receives a high support rate, will be recognized as the interpopulation consensus communities of the previous and current steps. Interpopulation consensus communities are the knowledge that can be transferred from the previous to the current step. The population of the current time step can evolve toward the direction similar to the population in the previous time step by retaining such interpopulation consensus community during the evolutionary process. Community structure is subjected to evaluation, update, and mutation events, which are directed by using interpopulation consensus community information during the evolutionary process. The experimental results over many artificial and real-world dynamic networks illustrate that the proposed method produces more accurate and robust results than those of the state-of-the-art approaches.
The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique ...for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.
The particle swarm optimization (PSO) algorithm has been the object of many studies and modifications for more than 25 years. Ranging from small refinements to the incorporation of sophisticated ...novel ideas, the majority of modifications proposed to this algorithm have been the result of a manual process in which developers try new designs based on their own knowledge and expertise. However, manually introducing changes is very time consuming and makes the systematic exploration of all the possible algorithm configurations a difficult process. In this article, we propose to use automatic design to overcome the limitations of having to manually find performing PSO algorithms. We develop a flexible software framework for PSO, called PSO-X, which is specifically designed to integrate the use of automatic configuration tools into the process of generating PSO algorithms. Our framework embodies a large number of algorithm components developed over more than 25 years of research that have allowed PSO to deal with a large variety of problems, and uses irace , a state-of-the-art configuration tool, to automatize the task of selecting and configuring PSO algorithms starting from these components. We show that irace is capable of finding high-performing instances of PSO algorithms never proposed before.
•Proposing centripetal accelerated particle swarm optimization (CAPSO) based on PSO and Newtonian’s Motion Laws.•Proposing the binary mode of CAPSO (BCAPSO) for solving problems in discrete search ...space.•Having good performance of CAPSO and BCAPSO in solving various nonlinear functions.
Meta-heuristic search algorithms are developed to solve optimization problems. Such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities. Swarm intelligence (SI) algorithms comprise a branch of meta-heuristic algorithms that imitate the behavior of insects, birds, fishes, and other natural phenomena to find solutions for complex optimization problems. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton’s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO) scheme, is introduced. CAPSO accelerates the learning and convergence of optimization problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces. These algorithms are evaluated using nonlinear benchmark functions, and the results are compared with the gravitational search algorithm (GSA) and PSO in both the real and the binary search spaces. Moreover, the performance of CAPSO in solving the functions is compared with some well-known PSO algorithms in the literature. The experimental results showed that the proposed methods enhance the performance of PSO in terms of convergence speed, solution accuracy and global optimality.
Computing the sensitivity vector in the traditional first order reliability method may provide inaccurate reliability outcomes for discrete performance functions and inefficient computation burden ...for high-dimensional problems. In this study, two improved particle swarm optimization algorithms are proposed to enhance the convergence rate with global optimal results during the structural reliability analysis. The abilities for convergence speed and global convergence of the particle swarm optimization algorithm are improved using a novel hybrid method called particle swarm optimization-based harmony search algorithm (PSO–HS), and enhanced particle swarm optimization (EPSO). The proposed methods use a dynamic self-adaptive term to execute the local adjusting process. Using twelve numerical-based engineering problems, the structural reliability frameworks developed based on modified versions of particle swarm optimization algorithms are compared to numerous FORM algorithms and the current metaheuristic methods. Results indicated that the novel proposed methods using the improved PSO algorithms are more robust and efficient than the analytical FORM methods for solving high-dimensional engineering problems. Furthermore, compared to the previous metaheuristic approaches, the suggested methods enabled faster convergence.
•Two optimization algorithms are proposed as novel hybrid FORM in structural reliability analysis.•Local adjusting process is proposed in hybrid FORM methods of EPSO and PSO–HS.•PSO–HS and EPSO compared with PSO, HS, IHS, IPSO, LS-PSO and six FORM algorithms.•Proposed methods are more efficient than FORM for high-dimensional problems.
This paper proposes a facial expression recognition system using evolutionary particle swarm optimization (PSO)-based feature optimization. The system first employs modified local binary patterns, ...which conduct horizontal and vertical neighborhood pixel comparison, to generate a discriminative initial facial representation. Then, a PSO variant embedded with the concept of a micro genetic algorithm (mGA), called mGA-embedded PSO, is proposed to perform feature optimization. It incorporates a nonreplaceable memory, a small-population secondary swarm, a new velocity updating strategy, a subdimension-based in-depth local facial feature search, and a cooperation of local exploitation and global exploration search mechanism to mitigate the premature convergence problem of conventional PSO. Multiple classifiers are used for recognizing seven facial expressions. Based on a comprehensive study using within- and cross-domain images from the extended Cohn Kanade and MMI benchmark databases, respectively, the empirical results indicate that our proposed system outperforms other state-of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.
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•Pyrolysis kinetics of lignocellulosic components in biomass are analyzed by evolutionary computation.•DTG curves shift to higher-temperature areas when heating rate increases due to ...thermal lag.•Pyrolysis of cellulose is described by a single reaction.•Pyrolysis of hemicelluloses and lignin can be split into 4 and 5 reactions, respectively.•Thermal decomposition temperatures of cellulose, hemicelluloses, and lignin are identified.
The kinetics of lignocellulosic biomass pyrolysis is beneficial for reactor design to efficiently produce biofuel and bioenergy. Pyrolysis is a well-developed thermochemical process that converts biomass into valuable gaseous products, bio-oils, and solid products. To understand the complex pyrolysis process of lignocellulosic biomass, three model components of cellulose, hemicelluloses (xylan), and lignin were pyrolyzed using a thermogravimetric analyzer. An independent parallel reaction (IPR) kinetic model was optimized using a particle swarm optimization (PSO) algorithm. The IPR kinetic models of cellulose, hemicelluloses, and lignin could be modeled with 1 pseudo-reaction, 4 pseudo-reactions, and 5 pseudo-reactions, respectively, and good fit qualities higher than 95% can be achieved (except a few cases for lignin). Four different heating rates of 1, 5, 20, and 40 °C·min−1 were applied to examine the effect of heating rate on the pyrolysis process. When increasing the heating rate, the derivative thermogravimetric (DTG) peaks shifted to a higher temperature range, stemming from the thermal lag between the samples and heating environment. Overall, the temperature ranges of the thermal decomposition for cellulose, hemicelluloses, and lignin were within 269–394, 170–776, and 127–791 °C, respectively.
There are two common challenges in particle swarm optimization (PSO) research, that is, selecting proper exemplars and designing an efficient learning model for a particle. In this article, we ...propose a triple archives PSO (TAPSO), in which particles in three archives are used to deal with the above two challenges. First, particles who have better fitness (i.e., elites) are recorded in one archive while other particles who offer faster progress, called profiteers in this article, are saved in another archive. Second, when breeding each dimension of a potential exemplar for a particle, we choose a pair of elite and profiteer from corresponding archives as two parents to generate the dimension value by ordinary genetic operators. Third, each particle carries out a specific learning model according to the fitness of its potential exemplars. Furthermore, there is no acceleration coefficient in TAPSO aiming to simplify the learning models. Finally, if an exemplar has excellent performance, it will be regarded as an outstanding exemplar and saved in the third archive, which can be reused by inferior particles aiming to enhance the exploitation and to save computing resources. The experimental results and comparisons between TAPSO and other eight PSOs on 30 benchmark functions and four real applications suggest that TAPSO attains very promising performance in different types of functions, contributing to both higher solution accuracy and faster convergence speed. Furthermore, the effectiveness and efficiency of these new proposed strategies are discussed based on extensive experiments.
•We consider both fuel costs and emissions, and find the best compromise value.•We introduce differential evolution operator into quantum particle swarm optimization (QPSO).•We introduce crossover ...operator into quantum particle swarm optimization (QPSO).•Adaptive control is adopted for crossover probability.
Consumption of traditional fossil energy has promoted rapid economic development and caused effects such as climate warming and environmental degradation. In order to solve the problem of environmental economic dispatch (EED), this paper proposes a DE-CQPSO (Differential Evolution-Crossover Quantum Particle Swarm Optimization) algorithm based on the fast convergence of differential evolution algorithms and the particle diversity of crossover operators of genetic algorithms. In order to obtain better optimization results, a parameter adaptive control method is used to update the crossover probability. And the problem of multi-objective optimization is solved by introducing a penalty factor. The experimental results show that: the evaluation index and convergence speed of the DE-CQPSO algorithm are better than QPSO (Quantum Particle Swarm Optimization) and other algorithms, whether it is single-objective optimization of fuel cost and emissions or multi-objective optimization considering both optimization objectives. A good compromise value is verified, which verifies the effectiveness and robustness of the DE-CQPSO algorithm in solving environmental economic dispatch problems. The study provides a new research direction for solving environmental economic dispatch problems. At the same time, it provides a reference for the reasonable output of the unit to a certain extent.
In nature, almost every organism ages and has a limited lifespan. Aging has been explored by biologists to be an important mechanism for maintaining diversity. In a social animal colony, aging makes ...the old leader of the colony become weak, providing opportunities for the other individuals to challenge the leadership position. Inspired by this natural phenomenon, this paper transplants the aging mechanism to particle swarm optimization (PSO) and proposes a PSO with an aging leader and challengers (ALC-PSO). ALC-PSO is designed to overcome the problem of premature convergence without significantly impairing the fast-converging feature of PSO. It is characterized by assigning the leader of the swarm with a growing age and a lifespan, and allowing the other individuals to challenge the leadership when the leader becomes aged. The lifespan of the leader is adaptively tuned according to the leader's leading power. If a leader shows strong leading power, it lives longer to attract the swarm toward better positions. Otherwise, if a leader fails to improve the swarm and gets old, new particles emerge to challenge and claim the leadership, which brings in diversity. In this way, the concept "aging" in ALC-PSO actually serves as a challenging mechanism for promoting a suitable leader to lead the swarm. The algorithm is experimentally validated on 17 benchmark functions. Its high performance is confirmed by comparing with eight popular PSO variants.
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