Multi-objective Artificial Bee Colony Optimization Research Articles

Multiobjective Optimization of Chaotic Image Encryption Based on ABC Algorithm and DNA Coding

As digital communication and storage continue to expand, the protection of image privacy information becomes increasingly critical. To safeguard sensitive visual information from unauthorized access, this paper proposes a novel image encryption scheme that integrates multiobjective Artificial Bee Colony (ABC) optimization algorithm and DNA coding. Multiple evaluation metrics including correlation relationship, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and information entropy are collaboratively optimized by the ABC algorithm. The proposed method begins with the application of the SHA-256 algorithm to generate keys and random sequences using chaotic systems. These sequences are then employed for shuffling, DNA coding, decoding, and diffusion, generating initial encrypted images. Subsequently, the encrypted images serve as individuals within the ABC algorithm to determine optimal parameters of the chaotic systems and the best ciphertext image. Simulation experiments demonstrate that the ciphertext images achieved excellent results in information entropy, pixel correlation coefficient, NPCR, and UACI. The integration of the multiobjective ABC optimization algorithm with DNA coding in our proposed image encryption scheme results in heightened security, as evidenced by superior performance in various metrics.

Multi-objective optimization of CMOS low noise amplifier through nature-inspired swarm intelligence

This paper presents the application of two swarm intelligence techniques, multi-objective artificial bee colony (MOABC) and multi-objective particle swarm optimization (MOPSO), to the optimal design of a complementary metal oxide semiconductor (CMOS) low noise amplifier (LNA) cascode with inductive source degeneration. The aim is to achieve a balanced trade-off between voltage gain and noise figure. The optimized LNA circuit operates at 2.4 GHz with a 1.8 V power supply and is implemented in a 180 nm CMOS process. Both optimization algorithms were implemented in MATLAB and evaluated using the ZDT1, ZDT2, and ZDT3 test functions. The optimized designs were then simulated using the advance design system (ADS) simulator. The results showed that the MOABC and MOPSO techniques are practical and effective in optimizing LNA design, resulting in better performance than previously published works, with a gain of 21.2 dB and a noise figure of 0.848 dB.

Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization Problem

This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail to satisfy constraints while achieving optimal performance. To address this issue, we propose a multi-objective artificial bee colony optimization algorithm with a classical multi-objective theme called fitness sharing. This approach helps the convergence of the Pareto solution set towards a single optimal solution that satisfies multiple objectives. This article introduces multi-objective optimization with an example of a non-dominated sequencing technique and fitness sharing approach. The experimentation is carried out in MATLAB 2018a. In addition, we applied the proposed algorithm to two different real-time datasets, namely the knapsack problem and the nurse scheduling problem (NSP). The outcome of the proposed MBABC-NM algorithm is evaluated using standard performance indicators such as average distance, number of reference solutions (NRS), overall count of attained solutions (TNS), and overall non-dominated generation volume (ONGV). The results show that it outperforms other algorithms.

An economic/emission dispatch based on a new multi-objective artificial bee colony optimization algorithm and NSGA-II

An economic/emission dispatch based on a new multi-objective artificial bee colony optimization algorithm and NSGA-II

Comparison of priori and posteriori approach of multi-objective optimization for WEDM on Ti6Al4V alloy

Titanium alloys are known to have some excellent properties, such as good biocompatibility, good fatigue resistance and high strength to weight ratio. Due to these properties, Ti6Al4V alloy is widely used in the biomedical field, aerospace and automobile industries. In this article, pulse on-time (TON), pulse off time (TOFF), and servo voltage (SV) were selected as process parameters for wire electric discharge machining (WEDM) on Ti6Al4V alloy. The material removal rate (MRR) and surface roughness (SR) were determined as responses. MRR and SR have been equated by a central composite design (CCD: a response surface method technique). Then multi-objective Artificial Bee Colony optimization (MO-ABC) with Gray relational analysis (GRA) was selected as a priori approach for multi-objective optimization. Also, a multi-objective grasshopper optimization algorithm (MO-GOA) has been chosen as a posterior approach for optimization. These two algorithms have been tested on various iterations and populations. Based on the elapsed time, it has been found that the priori approach of multi-objective optimization is better than the posterior approach of multi-objective optimization. When comparing these algorithms based on the results, it is obtained that the posterior approach gives a better combination of multiple results. The major outcome of the research is that the priori method is quick, while the posterior approach produces many promising solutions.

A Decomposition and Dominance-Based Multiobjective Artificial Bee Colony Algorithm for Multiple Sequence Alignment

The multiple sequence alignment (MSA) problem is essential in biological research for finding a specific relationship between the biologic sequences and their function. This study proposes a decomposition and dominance-based multiobjective artificial bee colony optimization algorithm for MSA (MOABC/D-MSA). MOABC/D-MSA uses three kinds of searching strategies to obtain a group of nondominated solutions with high quality and diversity of an MSA problem. A decomposition-based employed bee strategy is proposed to search for high-performance solutions of the MSA, while insuring their diversity. A nondominated sorting-based onlooker strategy searches for the solutions near the Pareto front (PF) to guide the subsequent searching. The scout bee strategy facilitates the algorithm to get out of the local optimal. A comparative experiment is implemented on BAliBASE 3.0, a benchmark for MSA algorithms. Experimental results show that the proposed algorithm has competitive performance with state-of-the-art metaheuristic algorithms. Furthermore, nondominated solutions of MOABC/D-MSA have a more uniform distribution in the objective space.

Optimal design of an adaptive robust controller using a multi-objective artificial bee colony algorithm for an inverted pendulum system

In this study, a multi-objective artificial bee colony (MOABC) optimization algorithm was utilized to improve the performance of an adaptive robust control technique. This approach is implemented using an inverted pendulum system. More precisely, the proposed controller is a combination of a decoupled sliding-mode controller (DSMC) and adaptation laws based on the gradient descent approach. To achieve optimum control operation, the MOABC, as a novel meta-heuristic method simulated from the smart foraging activity of honeybee groups, was employed to optimize the coefficients of the suggested controller. In this regard, the objective functions are determined as the integral time of the absolute value of the pole angle and cart position errors. Finally, the time responses of the system states and control effort are presented to prove the effectiveness and feasibility of the proposed strategy compared with other contemporary studies referenced in this paper.

Robust watermarking scheme based LWT and SVD using artificial bee colony optimization

<span>This paper presents a watermarking scheme for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce a robust watermarking method. Furthermore, for increasing security encryption of the watermark is done prior to the embedding operation. In the proposed scheme, the actual image is altered to four sub-band over three levels of lifting wavelet transform then the singular value of the watermark image is embedded to the singular value of LH sub-band of the transformed original image. In the embedding operation, multiple scaling factors are utilized on behalf of the single scaling element to get the maximum probable robustness without changing watermark lucidity. Multi-objective artificial bee colony optimization is utilized for the determination of the optimal values for multiple scaling components, which are examined against various types of attacks. For making the proposed scheme more secure, the watermark is encrypted chaotically by logistic chaotic encryption before embedding it to the host (original) image. The experimental results show excellent imperceptibility and good resiliency against a wide range of image processing attacks.</span>

Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer

Superheated steam temperature (SST) is one of the most critical parameters for the process safety, overall efficiency and pollution reduction of coal-fired power plants. However, SST control is challenging due to various disturbances and model uncertainties, especially in the face of the growing penetration of intermittent renewable energy into the power grid. To this end, a cascaded Disturbance Observer-PI (DOB-PI) control strategy is proposed to enhance control performance. The observer design and parameter tuning are carried out through mechanism analysis on the proposed structure. Furthermore, a robust loop shaping method is introduced as a hard constraint to balance the control performance and robustness. The controller parameters are optimized based on the multi-objective artificial bee colony optimization (MOABC) algorithm. Simulation results show that the proposed cascaded DOB-PI control strategy can significantly improve the disturbance rejection performance of both the inner- and outer-loops of the SST control system. This paper indicates promising prospects for the proposed method in future applications.

Adaptive multilevel thresholding based on multiobjective artificial bee colony optimization for noisy image segmentation

Multilevel thresholding is one of the effective image segmentation methods. However, it faces three big challenges: (1) how to adaptively determine the number of multiple thresholds; (2) how to overcome the sensitivity to image noise; (3) how to perf

Identifying influential spreaders using multi-objective artificial bee colony optimization

Advertisement over social networks is critical for many businesses, and selecting the initial set of influential nodes for which the advertising message is passed, is regarded as an important issue in this regard. Although various measures have been proposed for specifying the influentiality of a set, it is affected by different factors. In this paper, a multi-objective function is first defined as an influentiality measure, and finding such an initial is framed as an optimization problem. Then, using artificial bee colony optimization two approaches are proposed to solve the problem. In the first approach, influentiality of nodes is only taken into account, while in the second method a budget constraint is also considered. Different experiments on real networks are conducted to evaluate the proposed methods, where the obtained results show their outperformance over state-of-the-art influence maximization methods.

Optimal Exit Configuration of Factory Layout for a Safer Emergency Evacuation using Crowd Simulation Model and Multi-Objective Artificial Bee Colony Optimization

This work aims at providing a systematic method in producing a safer and optimal factory layout based on a crowd simulation model and the multi-objective artificial bee colony optimization technique. Apart, from ensuring the efficiency of manufacturing processes in planning a factory layout, it is also important that the safety aspect is taken into account. A factory is usually a closed working area consisting of machines, equipment, assembly lines as well as individual working space and other departments within the factory. In this environment, workers move around in the factory to perform different activities, and hence highly complex crowd behaviours that are influenced by the physical, social and psychological factors of the crowd might take place. Therefore, the layout of the factory must be carefully designed so that efficient movements of people can be obtained. Furthermore, during emergency situations that require efficient evacuation of workers from the factory building, a good factory layout will prevent or minimize the possibility of injuries during the evacuation process. This will reduce the evacuation egress time, which is the quantity used to evaluate the evacuation efficiency and the building’s level of safety. One of the techniques to assess the evacuation efficiency of a particular space configuration is by using the crowd simulation model. Recent evidences suggest that the representation of crowd dynamics using a simulation model is useful, where experiments with real humans are too dangerous and not practical to be implemented. This work explains the method to provide optimal exit door configurations for a factory layout by analyzing the crowd evacuation time and the discomfort level, where the proposed optimum exit configurations will be compared with the original configuration for a better evacuation efficiency.

Identifying key players in large social networks by using a multi-objective artificial bee colony optimization approach

Identifying a set of individuals that have an influential relevance and act as key players is a matter of interest in many real world situations, especially in those related to the Internet. Although several approaches have been proposed in order to identify key players sets, they mainly focus just on the optimization of a single objective. This may lead to a poor performance since the sets identified are not usually able to perform well in real life applications where more objectives of interest are taken into account. Multi-objective optimization seems the natural extension for this task, but there is a lack of this type of methodologies in the scientific literature. An efficient Multi-Objective Artificial Bee Colony (MOABC) algorithm is proposed to address the key players identification problem and is applied in the context of six networks of different dimensions and characteristics. The proposed approach is able to best identify the key players than the ones previously proposed, especially in the context of large social networks. The model performance of the proposed approach has been evaluated according to different quality metrics. The results from the MOABC execution show important improvements with respect to the best multi-objective results in the scientific literature, specifically, in average, 13.20% of improvement in Hypervolume, 120.39% in Coverage Relation and 125.52% in number of non-dominated solutions. Even more, the proposed algorithm is also more robust when repeating executions.

Artificial Bee Colony Optimization of NOx Emission and Reheat Steam Temperature in a 1000 MW Boiler

This paper puts forward a new viewpoint on optimization of boiler combustion, namely, reducing NOx emission while maintaining higher reheat steam temperature rather than reducing NOx emission while improving boiler efficiency like traditional practices. Firstly, a set of multioutputs nonlinear partial least squares (MO‐NPLS) models are established as predictors to predict these two indicators. To guarantee better predictive performance, repeated double cross-validation (rdCV) strategy is proposed to identify the structure as well as parameters of the predictors. Afterward, some controllable process variables, taken as inputs of the predictors, are then optimized by minimizing NOx emission and maximizing reheat steam temperature via multiobjective artificial bee colony (MO‐ABC). Results show that our rdCV‐MO‐NPLS model with MO‐ABC optimization methods can reduce NOx emission synchronously and improve reheat steam temperature effectively compared with nondominated sorting genetic algorithm II (NSGA‐II) and combustion adjustment experimental data on a real 1000 MW boiler.

Low Complexity Fan Filters Using Multiobjective Artificial Bee Colony Optimization Aided McClellan Transformation for Directional Filtering

This brief considers an effective design method for quadrantally symmetric and centrosymmetric fan-type 2-D filters with minimum contour approximation error, using McClellan transformation aided by multiobjective artificial bee colony (MOABC) optimization. The novelty of the proposed approach lies in the use of MOABC optimization with McClellan transformation to realize fan filters with the least contour deviation error compared to the existing transformation-based design techniques, and highly reduced implementation complexity with comparable magnitude errors with respect to the existing optimal design techniques. Pareto-dominance-based MOABC optimization is used to find the optimal design parameters for transformation. The feasibility of the proposed method is verified for fan filters with different passband angles. The effectiveness of the designed filter in texture identification of an image is also demonstrated.

Application research of multi-objective Artificial Bee Colony optimization algorithm for parameters calibration of hydrological model

Parameter optimization methods for hydrological models have an important impact for the hydrological forecasting. To achieve the parameters’ optimization and calibration for the distributed, conceptual watershed Xinanjiang model effectively and accurately, a multi-objective Artificial Bee Colony algorithm named RMOABC which adopts the mechanisms of regulation operator and Adaptive Grid is introduced in the paper. In the evolution of the algorithm, the regulation operator mechanism can balance the weights of local search and global search, and the Adaptive Grid mechanism is utilized to evaluate and maintain the Pareto solutions in the external archive. In the experiments, three commonly used multi-objective optimization algorithms, the NSGA-II, the e-MOEA and the SMPSO, with the RMOABC algorithm were applied in Heihe River Basin, and the parameter optimization problem of Xinanjiang hydrological model was taken as the application case for long-term runoff prediction to validate and compare their performance. The experiments results showed the RMOABC algorithm can provide more comprehensive and reliable parameters sets for practical hydrological forecasting in the study area with lower execution time.

Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach

Automatic text summarization methods are increasingly needed nowadays. Extractive multi-document summarization approaches aim to obtain the main content of a document collection at the same time that the redundant information is reduced. This can be addressed from an optimization point of view. There is a lack of multi-objective approaches applied in this context. In this paper, a Multi-Objective Artificial Bee Colony (MOABC) algorithm has been designed and implemented for this task. Experiments have been performed based on datasets from Document Understanding Conference (DUC) and model performances have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, as is usual in this knowledge field. The results of the proposed approach show important improvements, i.e., in average, 31.09% (8.43%) and 18.63% (6.09%) of improvement in ROUGE-2 (ROUGE-L) have been obtained with respect to the best single-objective and multi-objective results in the scientific literature. Even more, the proposed approach has been proven to produce more concentrated ROUGE values when the algorithm execution is repeated (between 620.63% and 1333.95% of reduction in the relative dispersion, that is, between 6 and 13 times better), leading to more robust results.

A multi-objective evolutionary artificial bee colony algorithm for optimizing network topology design

Abstract The topological design of a computer communication network is a well-known NP-hard problem. The problem complexity is further magnified by the presence of multiple design objectives and numerous design constraints. This paper presents a goal programming-based multi-objective artificial bee colony optimization (MOABC) algorithm to solve the problem of topological design of distributed local area networks (DLANs). Five design objectives are considered herein, namely, network reliability, network availability, average link utilization, monetary cost, and network delay. Goal programming (GP) is incorporated to aggregate the multiple design objectives into a single objective function. A modified version of MOABC, named as evolutionary multi-objective ABC (EMOABC) is also proposed which incorporates the characteristics of simulated evolution (SE) algorithm for improved local search. The effect of control parameters of MOABC is investigated. Comparison of EMOABC with MOABC and the standard ABC (SABC) shows better performance of EMOABC. Furthermore, a comparative analysis is also done with non-dominated sorting genetic algorithm II (NSGA-II), Pareto-dominance particle swarm optimization (PDPSO) algorithm and two recent variants of decomposition based multi-objective evolutionary algorithms, namely, MOEA/D-1 and MOEA/D-2. Results indicate that EMOABC demonstrated superior performance than all the other algorithms.

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC) inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

A novel design and implementation technique for low complexity variable digital filters using multi-objective artificial bee colony optimization and a minimal spanning tree approach

Farrow structure in canonic signed digit (CSD) space is an efficient approach for the design of real-time tunable finite precision variable digital filters (VDFs). A novel design method for Farrow structure based VDFs in CSD space with reduced hardware complexity is proposed in this paper. The design approach makes use of the multi-objective artificial bee colony (MOABC) optimization algorithm with an integer search space to find the optimal Farrow sub-filter coefficients. Further, a novel low complexity implementation approach for the finite precision VDF using a minimal spanning tree approach is also proposed. The minimal spanning tree approach deploys the shift inclusive differential coefficients (SIDCs) and the different shifted SIDCs with common sub-expression elimination (CSE) to optimize the multiple constant multiplications involved in the filter realization. The attractive feature of our proposed method using MOABC and SIDCs with CSE lies in the increased hardware complexity reduction of the VDFs, compared to the existing methods, which in turn reduces the hardware resource utilization and power consumption drastically compared to the continuous coefficient VDFs. The hardware implementation of the VDF using the proposed method has also been done using Xilinx ISE to analyse the reduction in the hardware complexity and dynamic power.