Multi-objective Artificial Bee Colony Research Articles

IMOABC: An efficient multi-objective filter-wrapper hybrid approach for high-dimensional feature selection

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter-wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.

Delivery Route Scheduling of Heterogeneous Robotic System with Customers Satisfaction by Using Multi-Objective Artificial Bee Colony Algorithm

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an unmanned aerial vehicle (UAV), which is capable of traversing terrain but has limitations in terms of energy and payload. The problem is formulated as an optimal route scheduling problem in a road network, where the goal is to find the route with minimum delivery cost and maximum customer satisfaction (CS) enabling the UAV to deliver packages to customers. We propose a new method of route scheduling based on an improved artificial bee colony algorithm (ABC) and the non-dominated sorting genetic algorithm II (NSGA-II) that provides the optimal delivery route. The effectiveness and superiority of the method we proposed are demonstrated by comparison in simulations. Moreover, the physical experiments further validate the practicality of the model and method.

Multi-objective artificial bee colony algorithm for reducing carbon emission and maximizing profit in a circular supply chain network

Multi-objective artificial bee colony algorithm for reducing carbon emission and maximizing profit in a circular supply chain network

Matheuristic and learning-oriented multi-objective artificial bee colony algorithm for energy-aware flexible assembly job shop scheduling problem

With the increase of mass customization, flexible job shop scheduling problem considering assembly stage has widely existed in many manufacturing industries, such as die-casting mould factories. This problem is to find a reasonable machine assignment and operation sequence both in fabrication and assembly stages and simultaneously maximize production efficiency. In reality, energy shortages and environmental pollution have given an impetus to the development of energy-aware production scheduling problems. In this study, we address an energy-aware flexible assembly job shop scheduling problem (EFAJSP) with the objectives of minimizing flow time and energy consumption and first develop a mixed-integer linear programming (MILP) model to solve EFAJSP problem. Then, the model-specific characteristics are extracted and applied to a matheuristic decoding method for exploring the Pareto optimal solution. Due to the complexity of EFAJSP problem, a matheuristic and learning-oriented multi-objective artificial bee colony algorithm (MLABC), which combines the advantages of mathematical programming, reinforcement learning and meta-heuristic algorithm, is proposed. In addition, an initialization, destruction/construction operator and population update operator are proposed and work together to improve the exploration and exploitation performance of the proposed MLABC. Finally, numerical experimental results demonstrate the effectiveness of the proposed MILP model and the superiority of the MLABC over other algorithms in the literature.

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 Framework for Trade-Off Among Pedestrian Delays and Vehicular Emissions at Signal-Controlled Intersections

Traffic congestion has several adverse effects on urban traffic networks. Increased travel times of vehicles, with the addition of excessive greenhouse emissions, can be listed as harmful effects. To address these issues, transportation engineers aim to reduce private car usage, reduce travel times through different control strategies, and mitigate harmful effects on urban networks. In this study, we introduce an innovative approach to optimizing traffic signal control settings. This methodology takes into account both pedestrian delays and vehicular emissions. Non-dominated sorting genetic algorithm-II and Multi-objective Artificial Bee Colony algorithms are adopted to solve the multi-objective optimization problem. The vehicular emissions are modeled through the MOVES3 emission model and integrated into the utilized microsimulation environment. Initially, the proposed framework is tested on a hypothetical test network, followed by a real-world case study. Results indicate a significant improvement in pedestrian delays and lower emissions.

A Multiplier-Less FRM-Based Reconfigurable Regulated Bank of Filter for Spectrum Hole Detection in IoT

A promising solution for the detection of spectrum holes in the Internet of Things networks is the cognitive radio (CR) system, which is used to identify spectrum holes effectively. The intention of this work is to design a low-complexity Reconfigurable Regulated Bank of Filter (RRBF) structure for spectrum hole detection in IoT networks. The RRBF structure is designed by utilizing the Frequency Response Masking (FRM) approach and the Cosine Modulation Technique (CMT). Using the RRBF structure, multiple sharp non-uniform channels are generated for efficient spectrum hole detection in IoT networks. With the aid of an example, the performance and computational complexity of the RRBF structure are demonstrated. The result shows that the RRBF structure has a fewer multipliers than other existing methods. To obtain hardware-efficient realization, the RRBF structure is made of multiplier-less by incorporating Canonical Signed Digit (CSD), Multi-Objective Artificial Bee Colony (MOABC), and Shift Inclusive Differential Coefficients (SIDC) with Common Sub-expression Elimination (CSE) optimization techniques.

An enhanced multi-objective artificial bee colony algorithm with non-dominated sorting strategy

<p>This paper presents an improved metaheuristic technique inspired by the foundational concepts of the artificial bee colony (ABC) algorithm adapted to deal with multi-objective optimization challenges. Our approach combines the main ideas of ABC with a non-dominated sorting strategy including aspects of Pareto dominance, crowding distance, and greedy selection method. Furthermore, the chosen non-dominated solutions are archived in a repository with a static size. The presented approach, multi-objective artificial bee colony (MOABC), is compared to other state-of-the-art algorithms including the non-dominated sorting genetic algorithm II (NSGA II) and the multi-objective particle swarm optimization (MOPSO). MOABC and selected algorithms from the literature are applied to five zitzler-deb-thiele (ZDT) Multi-objective benchmark functions. Then three key metrics are employed for performance evaluations: generational distance (GD), spread (SP), and hypervolume (HV). The simulation results suggest that the proposed method is competitive and presents an effective choice for tackling multi-objective optimization problems.</p>

Using common redundancy components for suppliers in a supply chain network design problem considering energy costs and environmental effects

Improving the reliability of factories and suppliers through appropriate allocation of redundancy components is crucial in responding to customer demand. However, if individual factories independently buy and allocate redundancy components, huge investments will be required. To optimize the reliability level and costs of the supply chain network, the present study allocates these components to suppliers based on customer demand. While allocating redundancy components to factories improves the chain's reliability, it increases energy consumption and greenhouse gas emissions. Therefore, a balance between cost, reliability, and pollution needs to be established. The study considers a three-level supply chain, including the supplier (factories), distributor, and retailer, with stochastic and normally distributed demand assumed for each retailer. To address demand fluctuations, the risk pooling effect is implemented. As the problem belongs to the class of NP-hard problems, the study develops three multi-objective meta-heuristic algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGAII), Archived Multi-Objective Simulated Annealing (AMOSA), and Multi-Objective Artificial Bee Colony (MOABC) to solve it. The performance of these algorithms is evaluated using the results of the complete enumeration method. The comparison results based on six multi-objective performance metrics show that the MOABC algorithm has better performance in finding high-quality solutions in less time, while the NSGAII algorithm provides more diverse Pareto optimal solutions.

Optimal Location and Sizing of Multi-Resource Distributed Generator Based on Multi-Objective Artificial Bee Colony Algorithm

Optimal Location and Sizing of Multi-Resource Distributed Generator Based on Multi-Objective Artificial Bee Colony Algorithm

A Novel Model for Dynamic Manufacturing Service Collaboration on Industrial Internet

Industrial Internet enables distributed manufacturing enterprises to efficiently and promptly respond to the requirements of stakeholders using a manufacturing service collaboration chain (MSCC) composed of networked enterprises. However, various dynamic uncertainties may interrupt the MSCC, such as device malfunctions, urgent order insertions, and dynamic logistics, resulting in inexactitude practical applications. This paper proposes a novel reliability-based dynamic manufacturing service collaboration optimization (R-DMSCO) model for uncertain manufacturing collaboration procedures on Industrial Internet. The RDMSCO model reformulates the MSCC reliability in the form of an expectation-standard deviation of uncertain job completion time described by discrete scenarios pertaining to the uncertain perturbation of processing time and logistics time. Subsequently, an enhanced multi-objective artificial bee colony (EMOABC) algorithm that embeds four improvements is intended to address the manufacturing service collaboration optimization (MSCO) problem. The experimental results demonstrate that EMOABC outperforms other typical multi-objective algorithms for MSCO problems. Additionally, the R-DMSCO model can cope with dynamic uncertainties with better robustness and stability than two other effective strategies for dynamic manufacturing service collaboration.

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction.

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

A crossover-based multi-objective discrete particle swarm optimization model for solving multi-modal routing problems

Passengers must use a set of modes and vehicles to reach their destination in complicated urban structures. Choosing an optimal route is a complicated optimization problem for these passengers. This study proposes a multi-objective optimization algorithm to solve the routing problem in the urban multi-modal network. The multi-modal network problem considered in this study is a transportation network with subway, Bus Rapid Transit (BRT), taxi, and walking modes. The objective functions determine the optimized route by considering route length, traffic, comfort, and safety. We develop a Crossover-Based Multi-Objective Discrete Particle Swarm Optimization (CBMODPSO) to solve the problem. CBMODPSO has been improved using mutation and crossover operators. Multi-Objective Artificial Bee Colony (MOABC), Multi-Objective Ant Colony Optimization (MOACO), Multi-Objective Biogeography-Based Optimization (MOBBO), Multi-Objective Gray Wolf Optimization (MOGWO), and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithms are used to evaluate and compare the results from the CBMODPSO algorithm. In addition, CBMODPSO results are compared with previous research results. We show the improved algorithm is more repeatable than other algorithms. The CBMODPSO algorithm has a faster convergence rate and is able to get to an optimal solution in a smaller generation number and much less time. The CBMODPSO algorithm is implemented in about one-thirties of the MOBBO duration. Meanwhile, it has a reproducibility of almost twice the MOGWO algorithm.

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.

Environmental economic dispatch method of power system based on multiobjective artificial bee colony algorithm

Environmental economic dispatch method of power system based on multiobjective artificial bee colony algorithm

Energy consumption calculation and energy-saving measures of substation based on Multi-objective artificial bee colony algorithm

Abstract Electric energy is the most commonly used energy in people’s daily life. In the process of use, how to improve the utilization rate of resources, so as to achieve the purpose of energy saving and environmental protection of power resources is a very significant work. The power grid is crucial in the transmission of electricity, and the energy consumption of substations is a major factor affecting the operation cost. Therefore, it is a key condition to ensure the stable operation of the power grid to scientifically calculate its energy consumption and formulate appropriate energy-saving measures. After consulting a large number of relevant documents and data, it has been found that many researchers have provided new ideas for the calculation of energy consumption and the study of energy-saving measures in substations. This article was adding its own understanding and taking it as the direction and basis. In the introduction, the research significance of substation energy consumption calculation was introduced, and then the academic research and analysis were carried out on the two key sentences of substation energy consumption calculation and analysis and the research of multi-objective artificial bee colony (ABC) algorithm in substation. In the method part, a constructive algorithm theory was proposed, and the application of the algorithm in substation energy consumption was proposed to provide a theoretical basis for the energy consumption calculation and energy conservation measures research of substations based on multi-objective ABC algorithm. At the end of the article, a simulation comparison experiment was carried out and the experiment was summarized and discussed. Firstly, the performance of the two algorithms was analyzed. Then, through the first selection of power plant T and Y, it was concluded that the resistivity of the cable in the distribution room in power plant Y was much higher than the average value based on the experimental results. Therefore, it was taken as the sample subject of the following experiments and analyzed again. For the second time, the analysis of district power plant Y showed that through simple calculation, the average energy efficiency difference of the distribution room before and after use was 3.1. Finally, it was analyzed that the ABC algorithm could not only improve the energy efficiency of the distribution room in the power plant, but also improve the effective efficiency of its power station. It shows that this kind of algorithm is effective and worthy of further study.

Improved multi-objective artificial bee colony algorithm for parallel machine lot-streaming scheduling problem with limited and unequal sub-lots

Job splitting is a full-time effective measure used to improve corporate competitiveness, especially when the lot size of jobs is considerably large. This work investigates a parallel machine lot-streaming scheduling problem, where unequal sub-lot is considered and the number of sub-lots is strictly limited by that of cutters. Focus on this problem, a mixed-integer linear programming model is formulated to minimize makespan and due time deviation simultaneously, to scrutinize the coupling relationship among decision variables, i.e., the number and size of sub-lots, the machine allocation, and the processing order of all sub-lots. Then, an improved multi-objective artificial bee colony algorithm (IMOABC) with two categories of improvements is developed to obtain high-quality Pareto front solutions. Specifically, a semi-random initialization is proposed to balance the workload as evenly as possible, which includes a half-normal distribution strategy for determining the number of utilized sub-lots, an equal-probability lot-splitting strategy for determining the size of each sub-lots, and a machine allocation strategy to synchronize the completion time of all machines. Besides, a multipoint preservative crossover is designed to enhance diversity in the employed bee phase, a sub-lot adjustment operator is proposed to implement objective-oriented local search in the onlooker bee phase, and an individual restart mechanism is adopted to avoid being trapped in local optimum in the scout bee phase. Extensive experiment results demonstrate that IMOABC significantly outperforms the other six state-of-the-art algorithms in terms of diversity and convergence.

Improved multi-objective artificial bee colony algorithm-based path planning for mobile robots.

Mobile robots are widely used in various fields, including cosmic exploration, logistics delivery, and emergency rescue and so on. Path planning of mobile robots is essential for completing their tasks. Therefore, Path planning algorithms capable of finding their best path are needed. To address this challenge, we thus develop improved multi-objective artificial bee colony algorithm (IMOABC), a Bio-inspired algorithm-based approach for path planning. The IMOABC algorithm is based on multi-objective artificial bee colony algorithm (MOABC) with four strategies, including external archive pruning strategy, non-dominated ranking strategy, crowding distance strategy, and search strategy. IMOABC is tested on six standard test functions. Results show that IMOABC algorithm outperforms the other algorithms in solving complex multi-objective optimization problems. We then apply the IMOABC algorithm to path planning in the simulation experiment of mobile robots. IMOABC algorithm consistently outperforms existing algorithms (the MOABC algorithm and the ABC algorithm). IMOABC algorithm should be broadly useful for path planning of mobile robots.

A Low-Complexity Multiplier-Less Filter Bank Based on Modified IFIR for the SDR Channelizer

Digital filter banks are extensively used in an SDR channelizer for channelization. The objective of this research work is to design a low computational complexity FIR filter bank for generating sharp transition width channels for SDR. The design of unified and variable bandwidth channels for SDR using the proposed structure is based on the modified IFIR filter structure and cosine modulation technique (CMT). The performance of the proposed structure is demonstrated with the help of an example. The results show that the multiplier complexity of the proposed structure is less than those of other state-of-the art methods. The optimization techniques are incorporated in this work to further reduce the complexity of the proposed structure. With the help of canonical signed digit (CSD), multi-objective artificial bee colony (MOABC) and shift inclusive differential coefficients (SIDC) – common sub-expression elimination (CSE) optimization, the filter used in this structure is made multiplier-less.

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.