A bare bones bacterial foraging optimization algorithm

Bacterial foraging optimization (BFO), based on the social foraging behaviors of bacteria, is a new intelligent optimizer. It has been widely accepted as an optimization algorithm of current interest for a variety of fields. However, compared with other optimizers, the BFO possesses a poor convergence performance over complex optimization problems. To improve the optimization capability of the BFO, in this paper a bare bones bacterial foraging optimization (BBBFO) algorithm is developed. First, a chemotactic strategy based on Gaussian distribution is incorporated into this method through making use of both the historical information of individual and the share information of group. Then the swarm diversity is introduced in the reproduction strategy to promote the exploration ability of the algorithm. The performance of BBBFO is verified on various benchmark functions, the comparative results reveal that the proposed approach is more superior to its counterparts.     

A novel machine learning approach for early detection of hepatocellular carcinoma patients

Liver cancer is quite common type of cancer among individuals worldwide. Hepatocellular carcinoma (HCC) is the malignancy of liver cancer. It has high impact on individual’s life and investigating it early can decline the number of annual deaths. This study proposes a new machine learning approach to detect HCC using 165 patients. Ten well-known machine learning algorithms are employed. In the preprocessing step, the normalization approach is used. The genetic algorithm coupled with stratified 5-fold cross-validation method is applied twice, first for parameter optimization and then for feature selection. In this work, support vector machine (SVM) (type C-SVC) with new 2level genetic optimizer (genetic training) and feature selection yielded the highest accuracy and F₁-Score of 0.8849 and 0.8762 respectively. Our proposed model can be used to test the performance with huge database and aid the clinicians.     

Model specification search using a genetic algorithm with factor reordering for a simple structure factor analysis model

Abstract:  Many techniques for automated model specification search based on numerical indices have been proposed, but no single decisive method has yet been determined. In the present article, the performance and features of the model specification search method using a genetic algorithm (GA) were verified. A GA is a robust and simple metaheuristic algorithm with great searching power. While there has already been some research applying metaheuristics to the model fitting task, we focus here on the search for a simple structure factor analysis model and propose a customized algorithm for dealing with certain problems specific to that situation. First, implementation of model specification search using a GA with factor reordering for a simple structure factor analysis is proposed. Then, through a simulation study using generated data with a known true structure and through example analysis using real data, the effectiveness and applicability of the proposed method were demonstrated.     

Responses to olfactory stimuli in spotted hyenas (Crocuta crocuta): II. Discrimination of conspecific scent

Scent marking in spotted hyenas (Crocuta crocuta) includes the deposition of anal sac secretions, or "paste," and presumably advertises territorial ownership. To test whether captive hyenas classify and discriminate individuals using odor cues in paste, the authors conducted behavioral discrimination bioassays and recorded hyena investigation of paste extracted from various conspecific donors. In Experiment 1, subjects directed most investigative behavior toward scents from unfamiliar hyenas and members of the opposite sex. In Experiment 2, male hyenas discriminated between concurrent presentations of paste from various unfamiliar females in similar reproductive states. Thus, pasted scent marks convey information about the sex, familiarity, and even identity of conspecifics. Aside from territory maintenance, scent marking may also communicate information about individual sexual status.     

A modified conjugate gradient-based Elman neural network

Elman recurrent network is a representative model with feedback mechanism. Although gradient descent method has been widely used to train Elman network, it frequently leads to slow convergence. According to optimization theory, conjugate gradient method is an alternative strategy in searching the descent direction during training. In this paper, an efficient conjugate gradient method has been presented to reach the optimal solution in two ways: (1) constructing a more effective conjugate coefficient, (2) determining adaptive learning rates in terms of the generalized Armijo search method. Experiments show that the performance of the new algorithm is superior to traditional algorithms, such as gradient descent method and conjugate gradient method. In particular, the new algorithm has better performance than the evolutionary algorithm. Finally, we prove the weak and strong convergence of the presented algorithm, i.e., the gradient norm of the error function with respect to the weight vectors converges to zero and the weight sequence approaches a fixed optimal point.     

Traction-energy balancing adaptive control with slip optimization for wheeled robots on rough terrain

On rough terrain, excessive wheel slippage is easily generated by changes of surface conditions such as soil types and geometries. It induces considerable loss of wheel traction and battery energy. To prevent this, wheeled robots should consistently recognize the current situation generated between wheel and surface. And also wheeled robots are required to optimally control wheel motion in limited wheel traction and battery capacity. Therefore, this paper proposes a novel wheel control algorithm based on slip optimization of traction and energy, which is adaptive to change of surface condition. Proposed wheel control algorithm is called Traction-Energy Balancing Adaptive Control (TEB) in this paper and TEB assigns optimized rotation speed to each wheel by observing wheel slip ratio which is a key parameter of TEB. As functions of TEB, TEB is largely divided into three main parts; (1) slip optimizer (2) slip controller (3) SC-compensator. In the slip optimizer, two optimal slip models were derived as a function of slip ratios regarding maximum traction and tractive efficiency using experimental data about wheel-terrain interaction in three types of soil (grass, gravel and sand). And the optimal slip models were employed in order to determine a desired slip value of wheel with observation of a change in actual robot velocity as control input in the slip controller. For optimal slip control, the proposed slip controller is based on conventional PID controller with compensating disturbance in the controller (SC-compensator) which occurs by change of surface shapes. In the SC-compensator, radial function networks (RBFN) was applied in the slip controller and RBFN was of help to readjust previously set PID gains depending on occurred slip error. Finally, TEB was experimentally verified by controlling a real robot having four wheels on various terrain types.     

An improved stochastic EM algorithm for large-scale full-information item factor analysis

In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt (1985) Computational Statistics Quarterly, 2, 73) for large-scale full-information item factor analysis. Innovations have been made on its implementation, including an adaptive-rejection-based Gibbs sampler for the stochastic E step, a proximal gradient descent algorithm for the optimization in the M step, and diagnostic procedures for determining the burn-in size and the stopping of the algorithm. These developments are based on the theoretical results of Nielsen (2000, Bernoulli, 6, 457), as well as advanced sampling and optimization techniques. The proposed algorithm is computationally efficient and virtually tuning-free, making it scalable to large-scale data with many latent traits (e.g. more than five latent traits) and easy to use for practitioners. Standard errors of parameter estimation are also obtained based on the missing-information identity (Louis, 1982, Journal of the Royal Statistical Society, Series B, 44, 226). The performance of the algorithm is evaluated through simulation studies and an application to the analysis of the IPIP-NEO personality inventory. Extensions of the proposed algorithm to other latent variable models are discussed.     

Particle swarm optimised analysis of investment decision

A portfolio forecasting model based on particle swarm optimization (PSO) algorithm with automatic factor scaling is proposed in this Article to effectively improve the accuracy of related analysis model in portfolio application. Firstly, the portfolio problem is analyzed and a hybrid constraint portfolio model is obtained by improving portfolio model with consideration of general portfolio model and combination of market value constraint and upper bound constraint according to Markowitz's theory. Secondly, PSO algorithm is introduced during analysis on portfolio model and solution is found with the hybrid constraint portfolio model of PSO on portfolio. In addition, in order to further improve the performance of PSO in model solution, automatic factor scaling is used for adaptive learning on parameters associated with PSO, to improve convergence of the algorithm. At last, simulation experiments show that the algorithm proposed can obtain a more ideal investment portfolio scheme, thereby reducing investment risks and obtaining greater investment returns.     

Detecting anonymous attacks in wireless communication medium using adaptive grasshopper optimization algorithm

Intrusion Detection Systems (IDSs) is a system that monitors network traffic for suspicious activity and issues alert when such activity is revealed. Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends. For this reason, in this study we developed a new multi-swarm adaptive grasshopper optimization algorithm to utilize adaptation mechanism in a group of swarms based on fuzzy logic to protect against sophisticated attacks. The proposed (MSAGOA) technique has the capability of global optimization and rapid convergence that are used to attain optimal feature subsets to identify attack types on IDS datasets. In the MSAGOA technique, learning engine as Extreme learning Machine, Naive Bayes, Random Forest and Decision Tree is applied as a fitness function to select the highly discriminating features and to maximize classification performance. Afterward, select the best classifier which works as a fitness function in our approach to measure the performance in terms of accuracy, detection rate, and false alarm rate. The simulations are performed on three IDS datasets such as NSL-KDD, AWID-ATK-R, and NGIDS-DS. The experimental results demonstrated that MSAGOA method has performed better and obtained high detection rate of 99.86%, accuracy of 99.89% in NSL-KDD and high detection rate of 98.73%, accuracy of 99.67% in AWID-ATK-R and detection rate of 89.50%, accuracy of 90.23% in NGIDS-DS. In addition, the performance is compared with several other existing techniques to show the efficacy of the proposed approach.     

Positive Group Affective Tone and Team Creative Performance and Change‐Oriented Organizational Citizenship Behavior: A Moderated Mediation Model

Despite the vast amount of research on creativity and organizational citizenship behavior (OCB), little knowledge has been accumulated with respect to underlying mechanisms and boundary conditions affecting team creative performance and change OCB. To fill this research gap, this study aims at proposing and testing a moderated mediation model that delineates the relationships among positive group affective tone (PGAT), team reflexivity, team leader transformational leadership, team creative performance, and team change OCB. As hypothesized, PGAT was positively associated with team reflexivity, which in turn significantly predicted team creative performance and change OCB. In addition, the relationship between PGAT and team reflexivity and the indirect effects of PGAT on team creative performance and change OCB through team reflexivity were more pronounced when team leader transformational leadership was high than when it was low. These findings were validated in a post hoc analysis that compared the proposed moderated mediation model with alternative models.     

Affective mechanisms linking dysfunctional behavior to performance in work teams: a moderated mediation study

The present study examines the association between dysfunctional team behavior and team performance. Data included measures of teams' dysfunctional behavior and negative affective tone as well as supervisors' ratings of teams' (nonverbal) negative emotional expressivity and performance. Utilizing a field sample of 61 work teams, the authors tested the proposed relationships with robust data analytic techniques. Results were consistent with the hypothesized conceptual scheme, in that negative team affective tone mediated the relationship between dysfunctional team behavior and performance when teams' nonverbal negative expressivity was high but not when nonverbal expressivity was low. On the basis of the findings, the authors conclude that the connection between dysfunctional behavior and performance in team situations is more complex than was previously believed--thereby yielding a pattern of moderated mediation. In sum, the findings demonstrated that team members' collective emotions and emotional processing represent key mechanisms in determining how dysfunctional team behavior is associated with team performance.     

It looks easy! Heuristics for combinatorial optimization problems

Human performance on instances of computationally intractable optimization problems, such as the travelling salesperson problem (TSP), can be excellent. We have proposed a boundary-following heuristic to account for this finding. We report three experiments with TSPs where the capacity to employ this heuristic was varied. In Experiment 1, participants free to use the heuristic produced solutions significantly closer to optimal than did those prevented from doing so. Experiments 2 and 3 together replicated this finding in larger problems and demonstrated that a potential confound had no effect. In all three experiments, performance was closely matched by a boundary-following model. The results implicate global rather than purely local processes. Humans may have access to simple, perceptually based, heuristics that are suited to some combinatorial optimization tasks.     

It looks easy! Heuristics for combinatorial optimization problems

Human performance on instances of computationally intractable optimization problems, such as the travelling salesperson problem (TSP), can be excellent. We have proposed a boundary-following heuristic to account for this finding. We report three experiments with TSPs where the capacity to employ this heuristic was varied. In Experiment 1, participants free to use the heuristic produced solutions significantly closer to optimal than did those prevented from doing so. Experiments 2 and 3 together replicated this finding in larger problems and demonstrated that a potential confound had no effect. In all three experiments, performance was closely matched by a boundary-following model. The results implicate global rather than purely local processes. Humans may have access to simple, perceptually based, heuristics that are suited to some combinatorial optimization tasks.     

Semi-sparse PCA

It is well known that the classical exploratory factor analysis (EFA) of data with more observations than variables has several types of indeterminacy. We study the factor indeterminacy and show some new aspects of this problem by considering EFA as a specific data matrix decomposition. We adopt a new approach to the EFA estimation and achieve a new characterization of the factor indeterminacy problem. A new alternative model is proposed, which gives determinate factors and can be seen as a semi-sparse principal component analysis (PCA). An alternating algorithm is developed, where in each step a Procrustes problem is solved. It is demonstrated that the new model/algorithm can act as a specific sparse PCA and as a low-rank-plus-sparse matrix decomposition. Numerical examples with several large data sets illustrate the versatility of the new model, and the performance and behaviour of its algorithmic implementation.

     

The Snake-in-the-Torus parameter-optimization algorithm: Initial evaluation

A new quasi-annealing parameter-optimization algorithm, which can be implemented with parallel processors, is explained. The performance of this algorithm is compared to that of the Simplex algorithm on a variety of nonlinear functions with constraints. In general, the algorithm is more robust, but computationally more expensive, than the Simplex algorithm. General issues discussed include algorithm speed and robustness, applicability to both computer and mathematical models, and parallel efficiency.     

Social feeding reverses learned flavor aversions in spotted hyenas (Crocuta crocuta).

Spotted hyenas are highly social carnivores with competitive feeding habits. It was hypothesized that social feeding might be capable of reversing an established flavor aversion. The dominant hyena of each of 4 captive social groups was given a novel food mixed with LiCl on Day 1; aversion to this food was demonstrated on Day 2. On Day 3, the dominant hyena and 3 or 4 members of her group were offered the food simultaneously. Three of the 4 dominant hyenas ate substantial quantities of the food during this group feeding, and all 4 hyenas ate the following day when offered the food alone. In contrast, when averted to a different novel food, the hyenas refused that food on 3 consecutive days. In this species social factors, such as those operating during group feeding, are sufficiently powerful to attenuate, immediately and dramatically, a strong LiCl-induced aversion.     

Scent marking (pasting) in a colony of immature spotted hyenas (Crocuta crocuta): a developmental study

Pasting, a stereotypic form of anal gland scent marking, was studied in 2 cohorts (N = 20) of captive spotted hyenas (Crocuta crocuta). A significant increment in the frequency of pasting occurred in both cohorts as the animals approached sexual maturity; however, gonadectomy during the early juvenile age period had no significant effects on subsequent pasting frequency. Dominant hyenas in both cohorts tended to scent-mark more frequently than subordinates during the late subadult period. Pasting was facilitated by the immediately preceding pasting activities of other hyenas, as has been reported to occur in nature, and olfactory investigation was the most common behavior preceding pasting.     

Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization

This paper presents a multiple-objective metaheuristic procedure—Pareto simulated annealing. The goal of the procedure is to find in a relatively short time a good approximation of the set of efficient solutions of a multiple-objective combinatorial optimization problem. The procedure uses a sample, of so-called generating solutions. Each solution explores its neighbourhood in a way similar to that of classical simulated annealing. Weights of the objectives, used for their local aggregation, are tuned in each iteration in order to assure a tendency for approaching the efficient solutions set while maintaining a uniform distribution of the generating solutions over this set. A computational experiment shows that the method is a better tool for approximating the efficient set than some previous proposals. © 1998 John Wiley & Sons, Ltd.     

工作连通行为的双刃剑效应：多重任务倾向的调节作用

随着移动通信工具飞速发展,“全天候保持工作连通”的新时代已到来。在此背景下,工作连通行为对员工工作与生活的影响受到广泛关注。基于工作要求-资源模型,通过258份三时点的领导-员工配对数据探究工作连通行为的双刃剑效应。路径分析结果表明,工作连通行为一方面会通过工作目标进展进而影响员工工作绩效(积极路径),另一方面又会通过工作负荷进而导致工作-家庭冲突(消极路径)。多重任务趋向在上述两条路径过程中发挥调节作用,对于高多重任务趋向的员工, 积极路径的效果被强化,而消极路径的效果被削弱。     

Poor social performance of lonely people: lacking a skill or adopting a role?

A substantial literature has shown that lonely people differ from nonlonely people on a variety of measures of social performance. These differences have usually been conceptualized as a social skills deficit, which implies that lonely people lack the ability to perform appropriate and effective social behavior. Rather than a lack of this ability, the authors hypothesize that the adoption of passive interpersonal roles predisposes lonely people to exhibit inadequate performance. In order to test this hypothesis, lonely and nonlonely subjects were assigned to one of two roles: They either listened (Condition Li) to an interaction partner describe a personal problem or they themselves described a personal problem (Condition Pr) to their partner. The subjects' interpersonal role produced a substantial effect on their social behavior. Subjects who listened to their partner describe a problem generated more solutions to a set of hypothetical situations, attended to their partners more adequately, and conversed longer than did subjects who described a personal problem. In contrast, lonely subjects did not differ from nonlonely subjects in their social performance within each particular role. Lonely and nonlonely subjects did differ, however, in their subjective evaluations of themselves and of their performance. These results illustrate the need for research to address both the interpersonal and the intrapersonal bases of social performance.