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There are many complex optimization problems in the real world, and various evolutionary algorithms are proposed to solve them. Recently, the many-objective evolutionary algorithm using a one-by-one selection strategy (1by1EA) adopts a convergence indicator and a distribution indicator to balance convergence and diversity. However, the algorithm is too random in initialization and the fitness evaluation of solutions in the mating selection is single, which leads to poor performance in solving large-scale problems. Therefore, this paper proposes an improved method called 1by1EA-CHV by using circle chaotic mapping and a solution ranking mechanism based on the hypervolume (HV) indicator. We first map each component of solutions into a certain value space to initialize the population. Then, we calculate the contribution of each partition divided based on HV and apply the aggregation method to guide the reallocation of fitness, which achieves the ranking of solutions by using it before the old calculation method. To validate the performance, experiments compared 1by1EA-CHV with 1by1EA and other seven many-objective algorithms on large-scale functions, and the differences between these algorithmswere analyzed statistically by a non-parametric test. The results showed the superiority of 1by1EA-CHV in solving large-scale many-objective optimization problems with up to 2000 decision variables. [ABSTRACT FROM AUTHOR]

Protein pupylation is a type of post-translation modification, which plays a crucial role in cellular function of bacterial organisms in prokaryotes. To have a better insight of the mechanisms underlying pupylation an initial, but important, step is to identify pupylation sites. To date, several computational methods have been established for the prediction of pupylation sites which usually artificially design the negative samples using the verified pupylation proteins to train the classifiers. However, if this process is not properly done it can affect the performance of the final predictor dramatically. In this work, different from previous computational methods, we proposed an enhanced positive-unlabeled learning algorithm (EPuL) to the pupylation site prediction problem, which uses only positive and unlabeled samples. Firstly, we separate the training dataset into the positive dataset and the unlabeled dataset which contains the remaining non-annotated lysine residues. Then, the EPuL algorithm is utilized to select the reliably negative initial dataset and then iteratively pick out the non-pupylation sites. The performance of the proposed method was measured with an accuracy of 90.24%, an Area Under Curve (AUC) of 0.93 and an MCC of 0.81 by 10-fold cross-validation. A user-friendly web server for predicting pupylation sites was developed and was freely available at http://59.73.198.144:8080/EPuL View Full-Text [ABSTRACT FROM AUTHOR]

As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail tofind the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH) algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses. [ABSTRACT FROM AUTHOR]

Internet Communication Technologies (ICTs) are making progress day by day, driven by the relentless need to utilize them for everything from leisure to business. This inevitable trend has dramatically changed contemporary digital behavior in all aspects. Undoubtedly, digital fingerprints will be at some point unwarily left on crime scenes creating digital information security incidents. On the other hand, corporates in the private sector or governments are on the edge of being exploited in terms of confidential digital information leakages. Some digital fingerprinting is volatile by its nature. Alternatively, once the power of computing devices is no longer sustainable, these digital traces could disappear forever. Due to the pervasive usage of Google Calendar and Safari browser among network communities, digital fingerprinting could be disclosed if forensics is carried out in a sound manner, which could be admitted in a court of law as probative evidences concerning certain cybercrime incidents. [ABSTRACT FROM AUTHOR]

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method. [ABSTRACT FROM AUTHOR]

An effective hybrid cuckoo search algorithm(CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new multigene genetic programming (MGGP) approach for estimation of elastic modulus of concrete. The MGGP technique models the elastic modulus behavior by integrating the capabilities of standard genetic programming and classical regression. The main aim is to derive precise relationships between the tangent elastic moduli of normal and high strength concrete and the corresponding compressive strength values. Another important contribution of this study is to develop a generalized prediction model for the elastic moduli of both normal and high strength concrete. Numerous concrete compressive strength test results are obtained from the literature to develop the models. A comprehensive comparative study is conducted to verify the performance of the models. The proposed models perform superior to the existing traditional models, as well as those derived using other powerful soft computing tools. [ABSTRACT FROM AUTHOR]

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH)method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill's position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods. [ABSTRACT FROM AUTHOR]