Interactive Artificial Bee Colony Research Articles

Scheduling of unrelated parallel machines considering sequence-related setup time, start time-dependent deterioration, position-dependent learning and power consumption minimization

This research presents an optimization problem associated with scheduling of unrelated parallel machines considering the simultaneous effects of start-time-related deterioration, position-related learning and sequence-related setup times, with the aim to achieve an optimized value for the mean weighted tardiness and power consumption minimization. A workaround is proposed and served as the converter for solving the Mixed Integer Programming (MIP) problem by the continuous-based metaheuristic algorithms. Knowing the NP-hard nature of the problem, three metaheuristic algorithms, namely, Genetic Algorithm (GA), Cat Swarm Optimization (CSO) and Interactive Artificial Bee Colony (IABC), are employed to obtain quality solutions within acceptable computation time. A unique elitism strategy is introduced in the conventional CSO to reduce the computational time of its seeking mode and to provide improved solution, with the new algorithm form called CSO-Elit. Furthermore, a compact solution expression form is proposed for the use of GA, CSO-Elit and IABC to reduce the number of constraints. With the newly proposed model, many essential constraints are automatically built into the solution representation. Accordingly, the efficiency of algorithms is compared with LINGO regarding the quality of solutions, computational time, and overall power consumption using two sets of problems in small and large scales. In solving the small-scale cases, no statistically meaningful difference was observed between LINGO, GA and CSO-Elit, while in large-scale instances CSO-Elit demonstrated higher performance. The contributions of this paper lie in modeling a complex parallel machines problem considering power consumption minimization and proposing a new form of CSO algorithm with elitism strategy to reduce the computational time.

Optimal Design of PID-Based Low-Pass Filter for Gas Turbine Using Intelligent Method

Due to the ever-increasing demands for electrical energy in industrial and domestic consumption, the use of gas turbines in power plants has great importance for the generation of electrical energy while taking less time. In this case, analyzing the role of gas turbines in the generation of electrical energy and their performance in the stability of power systems has special importance. Therefore, acquiring the suitable model for gas turbines and estimating various factors in modeling can consider as main part of power system stability. The purpose of this paper is originally a detailed dynamic modeling of a gas turbine based on the Rowen design and then controlling it to get most stability of power system by a new intelligent procedure. In next step, the most commonly accruing faults in gas turbine which can lead to control difficulty in power system are considered for analysis in the proposed simulation. Gas turbine in combined cycle power plant has ability for changing operation condition rapidly and more frequently. So its useful life is lesser than steam turbine. Gas turbine controller must increase its useful life because of high expenditure of gas turbine constructing. Damage mitigating control or life extend control is to design a controller to get better tradeoff between dynamic act and structural durability in a power system. Finally, the proposed interactive artificial bee colony method is employed to design a better controller and the design has been done under different working conditions to get the best results from proportional–integral–differential type controller parameters with low-pass filter compare to genetic algorithm and particle swarm optimization.

Multi-size patch based collaborative representation for Palm Dorsa Vein Pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm

This paper proposes a novel method, Multi-Size patch based Collaborative Representation based Classification (CRC) strategy by Enhanced Ensemble Learning, for palm dorsa vein pattern (PDVP) based human recognition employing thermal imaging. This thermal PDVP imaging based human recognition methodology has been specifically employed to encounter the challenging crisis of intrusion posed by imposters. To address the Small Sample Size (SSS) problem, intrinsic to many biometric applications, each image is transformed into multiple patches, leading to an increase in the total number of samples. In a bid to make the classification strategy less sensitive to the choice of patch-size, the present paper proposes a new enhanced ensemble learning for the patch based CRC (PCRC) algorithm, where margin maximization is carried out using exponential squared loss minimization. This work also proposes how this loss minimization can be achieved by a stochastic optimization algorithm and solves this problem using artificial bee colony (ABC) algorithm. In this context, a new ABC variant, called modified interactive artificial bee colony (MI-ABC) algorithm, has also been proposed, which has been demonstrated to outperform the basic ABC and its several modern variants. The proposed methodology has been implemented on a well–structured real database, developed in our laboratory using real subjects, and the results obtained in implementation phase clearly demonstrate that our proposed method could outperform its several competitors and achieve substantially high classification rates, for the problem under consideration.

An Improved Role Based Trust Management System using Interactive Artificial Bee Colony (I-ABC) Algorithm for Wireless Sensor Networks

The aim of this research study is to propose an improved role based trust management system for wireless sensor networks. The objective is attained using an interactive artificial bee colony algorithm which is used for path optimization. Also the purpose is to guarantee to provide robust and trustworthiness among the clusters formed in the wireless sensor network. The reputation among the sensor nodes is assured using the trust model. One of the main concerns of WSN, that have attracted research scholars, is the property to guarantee a less amount of security in a limited environment. Trust among the communicative nodes is one of the major issues that have to be given importance in wireless sensor network. A number of research works have concentrated on only Trust Management (TM) techniques without considering their roles. Existing trust management schemes do not provide significant reliability in all environments. In order to overcome these issues, Role Trust (RT) framework is presented, to select role for each nodes representing policies in a distributed authorized environment. RT integrates the features of role-based access control and TM schemes. This feature is particularly applicable for attribute based access control. Role trust management schemes generate rules, identify and assure roles based on the rules generation process and then clusters are identified for each trust model. Along with the trust model, this study focuses on efficient data path with reduced data loss. Hence, this study presents a novel Swarm Intelligence based Role Trust and Reputation Model (SIRTRM) to provide trust and reputation in WSNs. Simulations are carried out in NS2 environment and the results portrays the accuracy, robustness and lightness of the proposed model.

A Comparative Study on Adaptive Lifting Based Scheme and Interactive Artificial Bee Colony Algorithm for Image Compression

A Comparative Study on Adaptive Lifting Based Scheme and Interactive Artificial Bee Colony Algorithm for Image Compression

Interactive Artificial Bee Colony Supported Passive Continuous Authentication System

In this paper, an initiative passive continuous authentication (CA) system based on both hard and soft biometrics is presented. Human facial features are used as hard biometric information for the authentication process, and the clothes' color of a user is employed as the soft biometric information. The passive CA system keeps verifying, without interrupting the user from concentrating on his work. It also provides the capacity for the machine to recognize who is in front of the terminal, reduces the potential security leaks, and denies access to the invader with the stolen account and password. In this system, the face recognition core is implemented not only by the Eigenface method, but also assisted by the interactive artificial bee colony optimization algorithm. The proposed method is evaluated by the ORL face database and tested on the prototype CA system for computer security. The experimental results indicate that the accuracy of recognition is raised up to 3.13%, i.e., from 83.75% to 86.88%, with data from the ORL database, and it is improved by 34.53% on average in the real-time continuous authentication environment.

Economical impact of RFID implementation in remanufacturing: a Chaos-based Interactive Artificial Bee Colony approach

In the modern manufacturing arena, environmental and economical concerns draw considerable attention from both practitioners and researchers towards remanufacturing practices. The success of remanufacturing firms depends on how efficiently the recovery process is executed. Radio Frequency Identification (RFID) technology holds immense potential to enhance the recovery process. The deployment of RFID technology at reverse echelons has the advantage of having a real time system with reduced inventory shrinkage, reduced processing time, reduced labor cost, process accuracy, and other directly measurable benefits. In spite of these expected benefits, the heavy financial investment required in implementing the RFID system is a big threat for remanufacturing companies. This paper examines the economical impact of RFID adoption to remanufacturing. The aim of the research is to compare the basic and RFID-diffused reverse logistics model, and to quantitatively decide whether RFID implementation is economically viable. In order to meet these objectives, we have proposed a Chaos-based Interactive Artificial Bee Colony (CI-ABC) algorithm. Numerical results from using the CI-ABC for optimal performance are presented and analyzed. Comparison between the canonical Artificial Bee Colony and the Particle Swarm Optimization reveals the superiority of the CI-ABC for this application.

A Novel Fuzzy RPID Controller for Multiarea AGC with IABC Optimization

An Interactive Artificial Bee Colony (IABC) Optimization based fuzzy (IABCF) to tune optimal gains of a Robust Proportional Integral Derivative (RPID) controller is proposed for the solution of multiarea automatic generation control (AGC) simulation problem in a restructured power system. One of the important problems in the proposed method is the exact tuning of the RPID parameters for achieving the desired level of robust performance. The problem of robustly tuning of RPID based AGC design is formulated as an optimization problem according to the time domain-based objective function, which is solved by the IABC technique that has a strong ability to find the most optimistic results. The robustness and effectiveness of the proffered method are shown on a two and four areas deregulated power system with possible contracted scenarios under large load demand and area disturbances in comparison with the other methods through FD and ITAE performance indices. The evaluation results show that the proposed control strategy achieves good robust performance for worldwide experience of automatic generation control in restructured systems parameters and load changes in the presence of system nonlinearities.