Use Of Ant Colony Optimization Research Articles

Measuring Global Character Dimensions: An Ant Colony Optimization Approach toward Three Core Strength Scales

The Values in Action (VIA) framework encompasses 24 universally valued character strengths. Recent factor-analytic work has identified three global core strengths (metatraits) that proved to be well-interpretable and cross-culturally replicable: positivity, dependability, and mastery. However, there are no short scales to economically measure these core strengths that would encourage (large-scale) survey-based research on the global level of the VIA trait space. In the present study, we selected 18 items to measure the three metatraits from the 96-item IPIP-VIA-R inventory. To optimize the item selection while considering multiple psychometric criteria simultaneously, we made use of Ant Colony Optimization. Thereby, we obtained balanced-keyed scales that cover the heterogeneous constructs well, showed good model fit and reliability across six samples from Germany and the U.K. (total N = 2,754), and achieved scalar measurement invariance across countries. Furthermore, we demonstrated each scale’s validity by locating the three core strengths in a nomological net with personality and value metatraits, life satisfaction, and behavioral criteria. Available in the public domain in English and German, these both valid and economical core strength scales may further stimulate integrative research on personality and values.

Empirical Mode Decomposition with Imf Using Ant Colony Optimization

The serial remote sensing images allow us to view how a certain area has changed and grown across period. Even though these raw images have fewer opportunities to generate information and additional insight, serial remote sensing images (SRSI) offer a significant opportunity to generate clusters and patterns. The implementation of SRSI aggregation is supported by the developments of patterns in a variety of circumstances, which include urbanization, the spread of native vegetation, and farming. We propose a novel outlook to mining sequence patterns that makes use of Ant Colony Optimization and Empirical Mode Decomposition. The newly created Empirical Mode Decomposition (EMD) and Ant Colony Optimization (ACO) approach is integrated to extract the most important characteristics features of significantly accurate performance from serial remote sensing images based on distinct criteria. The outcomes demonstrate that the accuracy of projected sequence patterns can be greatly improved by combining a new hybrid method with EMD and ACO feature selection. Because of this, the method is appealing for employing a farmland dataset based on serial remote sensing images to create ground-based stream-flow for strategically extracting spatially sequential patterns despite requesting less computational mining time and cost.

Multi-Objective Optimization of External Louvers in Buildings

Because solar energy is among the renewable energies, it has traditionally been used to provide lighting in buildings. When solar energy is effectively utilized during the day, the environment is not only more comfortable for users, but it also utilizes energy more efficiently for both heating and cooling purposes. Because of this, increasing the building’s energy efficiency requires first controlling the amount of light that enters the space. Considering that the only parts of the building that come into direct contact with the sun are the windows, it is essential to make use of louvers in order to regulate the amount of sunlight that enters the building. Through the use of Ant Colony Optimization (ACO), the purpose of this study is to estimate the proportions and technical specifications of external louvers, as well as to propose a model for designing the southern openings of educational space in order to maximize energy efficiency and intelligent consumption, as well as to ensure that the appropriate amount of light is provided. According to the findings of this research, the design of external louvers is heavily influenced by a total of five distinct aspects: the number of louvers, the depth of the louvers, the angle of rotation of the louvers, the distance between the louvers and the window, and the reflection coefficient of the louvers. The results of the 2067 simulated case study show that the best reflection rates of the louvers are between 0 and 15 percent, and the most optimal distance between the louvers and the window is in the range of 0 to 18 centimeters. Additionally, the results show that the best distance between the louvers and the window is in the range of 0 to 18 centimeters.

A comparative study on Ant Colony Optimization algorithm approaches for solving multi-objective path planning problems in case of unmanned surface vehicles

The increased use of unmanned surface vehicles (USVs) to various applications in complex environments has led to the need of novel path planning approaches that can effectively address multi-objective path planning problems. Ant Colony Optimization (ACO) is a well-known and popular path search algorithm for graph-based problems that finds the shortest path. It has been widely used for solving path planning problems for robotic vehicles, and unmanned aircraft and surface vehicles, among others. To this end, this study focuses on extending the use of ACO to cope with multiple objectives by using fuzzy inference systems, such as Mamdani and Takagi–Sugeno–Kang (TSK), or the root mean square error (RMSE) criterion adopted to the state-of-the-art SIGPA algorithm. A comparative evaluation of these approaches is conducted in the context of solving different scenarios of multi-objective USV path planning problems. The results showed that ACO with Mamdani reached better performance in terms of solution quality compared to the other approaches under examination, while ACO with RMSE presented higher convergence speed and ACO with TSK balanced better among convergence speed and solution quality. Thus, each of the proposed approaches can be considered for multi-objective path planning of USVs depending the application needs.

Modelling and analysing the impact of Circular Economy; Internet of Things and ethical business practices in the VUCA world: Evidence from the food processing industry

As the business ecosystem is very becoming volatile with uncertainty accompanied with poor process centric practices leading to complexity and contributing to ambiguous decision making. Such a scenario constitutes a Volatile, Uncertain, Complex and Ambiguous world known as VUCA world. To manage the business in VUCA world, companies have started adopting emerging technologies such as Circular Economy (CE) and Internet of Things (IoT). These technologies combined with a proactive decision-making process promote Ethical Business Practices (EBP) that can lead a sustainable business operation in the market. Therefore, the present study assesses the impact of CE; IoT and EBP (CE-IoTEBP) in the food processing industry. Here, the factors that are influencing the company’s preference towards the CE-IoTEBP were analysed. Further to evaluate CE-IoTEBP, an evaluation model was developed. Using, Churchill approach, exhaustive list of factors (79 factors) relevant to the model were identified. Then, Ant colony optimisation was used to reduce the number of factors (39 factors) to make the model robust and apt for the evaluation of CE-IoTEBP. This was followed by using the Fuzzy ANN system for classifying the factors based on the level of contributions to assess the level of adoption intention of CE-IoTEBP in the industry. Through, fuzzy decoding (FANN) the final value for the CE-IoTEBP adoption intention was obtained. Additionally, this research proposes a deployment model for evaluating the CE-IoTEBP in the food processing industry to improve the low contributing factors. The use of Ant Colony Optimisation (ACO) method makes the decision making significantly robust and the ANN system improves its efficiency. The deployment model can be a reference for both academicians and practitioners to emulate in different industries.

Ant colony optimization for variable selection in discriminant linear analysis

AbstractA new algorithm using ant colony optimization (ACO) for selection of variables in linear discriminant analysis (LDA) is presented. The role of ACO is explored in the context of LDA classification in which spectral variable multicollinearity is a known cause of generalization problems. The proposed ACO‐LDA presents a metaheuristic that mimics the ant's cooperative behavior, randomly depositing pheromones at vector elements corresponding to the most relevant variables. Such cooperative ant‐like behavior, which is absent in the genetic algorithm, increases the probability of discarding noninformative variables, favoring construction of more parsimonious models than genetic algorithm–linear discriminate analysis (GA‐LDA). The classification performance of ACO‐LDA is assessed in two case studies: (i) classification of edible vegetable oils (with respect to base oil) via ultraviolet–visible (UV‐Vis) spectrometry and (ii) simultaneous classification of tea samples with respect to type and geographic origin via near‐infrared (NIR) spectrometry. In the first study, ACO‐LDA was tested in a data set involving wide absorption bands in the UV region with low‐resolution and strong spectral overlapping. In the second study, its capacity to manage a data matrix with high dimensionality was evaluated. In both studies, ACO‐LDA selected a small subset of variables, which led to correct classifications for almost all of the samples, achieving a performance level similar to the well‐established partial least squares–discriminant analysis (PLS‐DA), and considerably better than GA‐LDA. The use of ACO to select LDA classification variables can minimize generalization problems commonly associated with multicollinearity.

The Predictive Power of K-Nearest Neighbor (KNN): The Effect of Corporate Governance Mechanisms on Earnings Management

This study investigates the long-run effect of corporate governance mechanisms on earnings management of listed companies in Nigeria and Ghana. The study uses Ant Colony Optimization (ACO) and K-Nearest Neighbor (KNN) in establishing a long-run effect of good corporate mechanisms in reducing earnings management practice by corporate managers. ACO selected four major corporate governance mechanisms: Board Procedure Index, Board Disclosure Index, Ownership Structure Index, and Shareholders’ Rights Index; these were the key corporate governance mechanisms that influence the reduction in earnings management activities. KNN produced a strong significant longitudinal effect of implementing good corporate governance mechanisms in decreasing the manipulating behavior of managers. Quality corporate governance mechanisms’ implementation reduces the opportunistic behavior of corporate managers in manipulating earnings. Therefore, the study alert policymakers the urgency in setting up appropriate policies to enhance the reduction in earnings management practices to provide accurate financial information for stakeholders’ financial decision-making. The use of ACO and KNN in the study is a great novelty, which presents a calibration and prediction of the impact of corporate governance mechanisms on earnings management showing the rate of reduction.

Ant Colony Optimization Using Different Heuristic Strategies for Capacitated Vehicle Routing Problem

Capacitated Vehicle Routing Problem (CVRP) is a variant of vehicle routing problem (VRP) in which vehicles with restricted capacities required to pick-up or deliver at various locations. The main constraint in CVRP is to pick-up or deliver the goods for the least cost without exceeding the vehicle capacity. Therefore, the main objective of this paper is to minimize the distance travelled by vehicles. Hence, this paper proposed to use Ant Colony Optimization (ACO) with different heuristic strategies to optimize the distance travelled by the vehicles while not exceeding the vehicle capacities. Swapping, reversion, and insertion are the heuristic strategies used to examine the efficiency of neighbour creations in ACO. Christofides data sets are utilized in this paper to experiment on the solution construction in ACO with different heuristic strategies. The results showed that the use of ACO is efficient using the swap, reverse and insert strategies for distance minimization but there are possibilities for the vehicle visiting the same customer more than once. Meanwhile ACO with random combination with swap, reverse and insert are capable to solve CVRP without any possibilities for the vehicle visiting the same customer more than once.

Distributed learning automata-based scheme for classification using novel pursuit scheme

Learning Automata (LA) is a popular decision making mechanism to “determine the optimal action out of a set of allowable actions” (Agache and Oommen, IEEE Trans Syst Man Cybern-Part B Cybern 2002(6): 738–749, 2002). The distinguishing characteristic of automata-based learning is that the search for the optimising parameter vector is conducted in the space of probability distributions defined over the parameter space, rather than in the parameter space itself (Thathachar and Sastry, IEEE Trans Syst Man Cybern-Part B Cybern 32(6): 711–722, 2002). Recently, Goodwin and Yazidi pioneered the use of Ant Colony Optimisation (ACO) for solving classification problems (Goodwin and Yazidi 2016). In this paper, we propose a novel classifier based on the theory of LA. The classification problem is formulated as a deterministic optimization problem involving a team of LA that operate collectively to optimize an objective function. Although many LA algorithms have been devised in the literature, those LA schemes are not able to solve deterministic optimization problems as they suppose that the environment is stochastic. In this paper, we develop a novel pursuit LA which can be seen as the counterpart of the family of pursuit LA developed for stochastic environments (Agache and Oommen, IEEE Trans Syst Man Cybern Part B Cybern 32(6): 738–749, 2002). While classical pursuit LA are able to pursue the action with the highest reward estimate, our pursuit LA rather pursues the collection of actions that yield the highest performance. The theoretical analysis of the pursuit scheme does not follow classical LA proofs and can pave the way towards more schemes where LA can be applied to solve deterministic optimization problems. When applied to classification, the essence of our scheme is to search for a separator in the feature space by imposing a LA based random walk in a grid system. To each node in the gird we attach an LA, whose actions are the choice of the edges forming the separator. The walk is self-enclosing, i.e., a new random walk is started whenever the walker returns to starting node forming a closed classification path yielding a multiedged polygon. In our approach, the different LA attached at the different nodes search for a polygon that best encircles and separates each class. Based on the obtained polygons, we perform classification by labelling items encircled by a polygon as part of a class using ray casting function. Seen from a methodological perspective, PolyPursuit-LA has appealing properties compared to SVM. In fact, unlike PolyPursuit-LA, the SVM performance is dependent on the right choice of kernel function (e.g. Linear Kernel, Gaussian Kernel)— which is considered a “black art”. PolyPursuit-LA can find arbitrarily complex separators in the feature space. Experimental results from both synthetic and real-life data show that our scheme is able to perfectly separate both simple and complex patterns outperforming existing classifiers, including polynomial and linear SVM, without the need of any “kernel trick”. We believe that the results are impressive given the simplicity of PolyPursuit-LA compared to other approaches such as SVM.

Self Organising Fuzzy Logic Classifier for Predicting Type-2 Diabetes Mellitus using ACO-ANN

In today’s digital world, a dataset with large number of attributes has a curse of dimensionality where the computation time grows exponentially with the number of dimensions. To overcome the problem of computation time and space, appropriate method of feature selection can be developed using metaheuristic approaches. The aim of this work is to investigate the use of ant colony optimization with the help of neural network to select near optimal feature subset and integrate it with the self-organizing fuzzy logic classifier for improving the recognition rate. The proposed fuzzy classifier derives prototype from the collected data through an offline training process and uses it to develop a fuzzy inference system for classification. Once trained, it can continuously learn from streaming data and later adapts the changing facts by updating the system structure recursively. The developed model is not based on predefined parameters used in the data generation model but is derived from the empirically observed data.

ACO–MKFCM: An Optimized Object Detection and Tracking Using DNN and Gravitational Search Algorithm

Object tracking is a dynamic optimization process based on the temporal information related to the previous frames. Proposing a method with higher precision in complex environments is a challenge for researchers in the field of study. In this research, efficient object detection and movement tracking video is proposed. Here we are considering the input video sequence is PETS and Hall monitor video. Kernel fuzzy c-means procedure still endures several downsides, such as reduced convergence rate, obtaining stuck in the local minima as well as at risk to initialization level of sensitivity. Ant colony optimization algorithm is a new population-based optimization technique that has actually been utilized effectively for resolving numerous complicated difficulties. This research suggested a new strategy called ACO–MKFCM to resolve MKFCM initialization issue making use of ant colony optimization (ACO) algorithm to locate optimum first cluster centres for the MKFCM, hence enhance all applications associated fuzzy clustering such as foreground segmentation in image processing. Initially, the background and foreground separation is done by hybridization of modified kernel fuzzy c means algorithm (MKFCM) with ant colony optimization. The recommended new technique is intellectual and also vibrant clustering method for splitting up of non-static object. Then object detection and tracking is done by gravitational search algorithm based deep belief neural network. The implementation will be in MATLAB. The performance of the suggested technique is appraised by means of precision, recall, F-measure, FPR, FNR, PWC, FAR, similarity, specificity, and accuracy. From the empirical effects, the future work outperforms than the state of art work. Here the proposed method attains maximum precision and recall value for both PETS and Hall monitor video when analyzed to the existent algorithm.

A cryptanalytic attack of simplified-AES using ant colony optimization

Ant colony Optimization is a nature-inspired meta-heuristic optimization algorithm that gained a great interest in resolution of combinatorial and numerical optimization problems in many science and engineering domains. The aim of this work was to investigate the use of Ant Colony Optimization in cryptanalysis of Simplified Advanced Encryption Standard (S-AES), using a known plaintext attack. We have defined the essential components of our algorithm such as heuristic value, fitness function and the strategy to update pheromone trails. It is shown from the experimental results that our proposed algorithm allow us to break S-AES cryptosystem after exploring a minimum search space when compared with others techniques and requiring only two plaintext-ciphertext pairs.

Software Test Suite Minimization using Ant Colony Optimization

In the fast pacing technological era, the key to a successful software industry is quick delivery of high quality software to the clients. This high quality is achieved by performing software testing on the product. The high quality product ensures stakeholder’s satisfaction which in turn spreads good word about the software industry making it a success. In this paper, we will focus on the problems faced during regression testing and how the same can be handled. Regression testing is a critical activity done during the software maintenance phase of the software development cycle. However, it has countless underlying issues like effective test case generation and prioritization, etc which need to be dealt with. These issues demand effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of Ant Colony Optimization (ACO) for test suite minimization has been an area of interest for many researchers. This paper presents an implementation of ACO for test suite minimization, showcasing how arbitrary nature of ACO helps choose an optimal solution to the problem.

Use of ant colony optimization and the Kalman filter to deduce thigh dip angle via acceleration and angular velocity sensing

Background: Acceleration and angular velocity sensors are commonly used in the measurement of gait parameters. A representative application calculates the limb segment dip angle. The rotation angle is typically deduced by a conventional Kalman filter, which includes the use of two empirically derived parameters. We improved this conventional method by introducing colony algorithm to find the optimal parameter combination instead of empirically assignment. Method: To achieve optimal results, a servo motor with an inertial measurement unit was used to simulate human limb segment motion according to programmed rotation angles that was employed as the ground truth. To minimize the bias between the ground truth and the calculated result, the ant colony algorithm was employed to obtain the optimal Kalman filter parameter combination in two-dimensional space. Results: By the motor experiment, the sum of the angle squared error was only 1.9305 rad2, much better than the 6.7723 rad2 error by the conventional method. The optimal parameter combination obtained was then used in a human experiment involving a basketball player. The frames from video of a whole gait cycle period were all showed with a corresponding deduced thigh dip angle curve diagram. Conclusion: The colony algorithm for parameters optimization results in less angle errors deduced by Kalman filter using the data from inertial measurement unit. The subject experiment verified the feasibility and performance of this method.

Use of Ant Colony Optimization (ACO) for Post-Deployment Replanning of UMTS Networks

Inherent dynamism in user movement demands for post-deployment tuning of UMTS networks to minimize the total cost of ownership (TCO). Conventionally, UMTS operators so far have considered many-to-one mapping of RNCs to MSCs. However, such single-homed networks do not remain cost-effective over the passage of time, typically when subscribers later on begin to show specific mobility patterns such as diurnal movement. This necessitates topological extension of the network in terms of dual-homing of some selected RNCs to two MSCs simultaneously via direct fibre links, resulting in a many-to-two mapping in parts of the network. The aim of such selective dual-homing is to reduce handoff cost maximally at the expense of minimal increase in link cost, thereby reducing the TCO optimally. In this article, the authors have formulated the scenario as an integer linear programming problem, converted it into a state space search and then solved it using Ant Colony Optimization (ACO) technique. Compared to Simulated Annealing (SA) and Tabu Search (TS), ACO exhibits 10% to 15% improvement in solution quality.

Medical image denoising based on 2D discrete cosine transform via ant colony optimization

In medical imaging, researchers usually encounter with different types of noise; for eliminating this noise, different methods have been suggested in both spatial and frequency domains. In this paper, a new method is proposed for removing Gaussian noise from medical images using two dimensional discrete cosine transform (2DDCT) and ant colony optimization (ACO) algorithm. In this algorithm, we attempted to identify the important frequency coefficients with the use of ant colony optimization and to eliminate the effects of noise by removing high frequency parts. Our proposed algorithm have been tested for various densities of the Gaussian noise and the experimental results show performance improvement in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM).

Ant Colony Optimization towards Image Processing

Soft Computing refers to the techniques of problem solving which are inspired from the human behavior, natural genetics and the behavior of insects. All these techniques are parallel computational techniques which aim to handle imprecise, incomplete, non-linear and complex data. This paper deals with one of the fields of soft computing- namely Ant Colony Optimization (ACO). ACO is a computational intelligence based approach which is used to solve combinatorial optimization problem. Due to its simplicity and optimal approach it has been applied to routing, scheduling, sub-set, assignment and classification problems.Focus of the current paper is onto the use of Ant Colony Optimization in the field of Image Processing. Edge detection, edge linking, feature extraction, segmentation and image compression are the various image processing tasks in which ACO has been applied successfully. The details pertaining to each of the approach have been discussed. Benefits of using ACO over the conventional techniques have also been presented.

Predicting and mapping of soil particle‐size fractions with adaptive neuro‐fuzzy inference and ant colony optimization in central Iran

In arid regions, knowledge of the variation in soil texture is crucial for land management because it affects soil physical, chemical, biological and most importantly hydrological properties. The availability of information on soil texture is scarce even though it is required to support land‐use management and sustainable development. Because it is costly to obtain information about the individual particle‐size fractions (PSFs), we used digital soil mapping methods (DSM) with environmental covariates that are less costly to obtain. Specifically, we explored the use of a digital elevation model and remote sensing data as environmental covariates to predict the vertical (i.e. 0–0.15, 0.15–0.3, 0.3–0.6 and 0.6–1 m) and lateral variation in PSFs over a 150‐km2 area in central Iran. We used a combination of equal‐area spline depth functions and three data‐mining techniques: multiple linear regression (MLR), artificial neural networks (ANN) and the neuro‐fuzzy inference system (ANFIS). In addition, we explored the effect of the reduction in dimension of feature space with ant colony optimization (ACO) and correlation‐based feature selection (CFS) on the accuracy of prediction of spatial models for each PSF. The results showed that the prediction of clay at 0–0.15‐m depth with ACO indicated the importance of including Landsat ETM+, the digital numbers of band 7 of Landsat images (B7) and clay index, whereas at 0.60–1‐m depth the wetness index and multi‐resolution valley bottom flatness index (MRVBF) were important. Model evaluation by leave‐one‐out cross‐validation with 191 soil observations indicated that the predictions by the ACO‐based ANFIS model (RMSE = 4.51% and R2 = 0.74 for clay at 0–0.15‐m) were more accurate than those by MLR and ANN. Spatial prediction was also better for the topsoil (0–0.15‐m) than at depth (RMSE = 7.1% for clay at 0.6–1 m); therefore, we conclude that the environmental covariates tested cannot resolve subsurface variation as accurately. Nevertheless, we recommend prediction by the ACO‐based ANFIS model and splines of lateral and vertical distribution of PSFs in other arid regions of Iran with the same agro‐ecological conditions.HighlightsDigital soil mapping of particle size‐fractions (PSF) by adaptive neuro‐fuzzy inference and ant colony optimization. Use of ant colony optimization (ACO) to assist in feature selection of environmental covariates. Neuro‐fuzzy inference system (ANFIS) superior to multiple linear regression (MLR) and artificial neural networks (ANN). PSF prediction by ACO‐based ANFIS model and splines is optimal.

Handling Selfishness False Alarm in Data Replica Allocation Method over MANET

In recent times, Mobile Ad-hoc Networks (MANET) accomplished important concentration, since it can be competent to offer scalable purposes and amplifies mobile devices. With the use of wireless networks, mobile hosts could alter their positions when protecting network associations in MANET. A MANET comprises of a collection of assisting wireless mobile nodes that energetically builds a short survive and self-organizing network devoid of the prop up of a centralized network arrangements. On the other hand the network has subjective with consistency and power utilization in previous research. To conquer the above stated concern, group based data duplication method and watchdog techniques are initiated. Nevertheless, it does not capable to offer improved response time and obtainable resources. In addition, the previous situation is not appropriate for managing the false alarms. For resolving these troubles, in the anticipated situation, group formation for response time and resource accessibility procedures are concerned. The false alarms are noticed and node disconnections are exposed with the use of hop count distance determination technique. It is utilized to eradicate the self-centered nodes in the specified network and amplifies the network recitals. The model allotment is performed by the use of Ant Colony Optimization (ACO). The ACO procedure produces ideal function and offers quicker response time along with resource accessibility. It chooses the finest group head for preferring non self-centeredness nodes which is utilized for data model allotment. The imitation outcome illustrates that the anticipated scheme offers maximum packet delivery ratio and lesser end to end holdup, energy utilization and operational cost. It demonstrates that the anticipated scheme has forceful network presentation rather than previous investigations.

Adaptive management of multimodal biometrics fusion using ant colony optimization

This paper presents a new approach for the adaptive management of multimodal biometrics to meet a wide range of application dependent adaptive security requirements. In this work, ant colony optimization (ACO) is employed for the selection of key parameters like decision threshold and fusion rule, to ensure the optimal performance in meeting varying security requirements during the deployment of multimodal biometrics systems. Particle swarm optimization (PSO) has been widely utilized for the optimal selection of these parameters in the earlier attempts in the literature [Veeramachaneni et al., 2005] and [Kumar et al., 2010]. However, in PSO these parameters are computed in continuous domain while they are assumed to be better represented as discrete variables [Kumar et al., 2010]. This paper therefore proposes the use of ACO, in which discrete biometric verification parameters are computed to ensure the optimal performance from the multimodal biometrics system. The proposed ACO based framework is also extended to the pattern classification approach where fuzzy binary decision tree (FBDT) is utilized for two-class biometrics verification. The experimental results are presented on true multimodal systems from various publicly available databases; IITD databases of palmprint and iris, XM2VTS database of speech and faces, and the NIST BSSR1 databases of faces and fingerprint images. Our experimental results presented in this paper suggest that (i) ACO based approach is capable of operating on significantly small error rates in comparison to the widely employed PSO for automated selection of biometrics fusion rules/parameters, (ii) the score-level fusion yields better performance with lower error rate in comparison to the decision level fusion, and finally (iii) the FBDT based classification approach delivers considerably superior performance for the adaptive biometrics verification.