Discrete Artificial Bee Colony Algorithm Research Articles

Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm

AbstractInferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single‐cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score‐based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi‐parameter fluorescence‐activated cell sorter dataset, indicate that PDABC surpasses the existing state‐of‐the‐art methods in terms of performance and computational efficiency.

A variable-representation discrete artificial bee colony algorithm for a constrained hybrid flow shop

In the realm of hybrid flow shop environments, existing literature predominantly overlooks a practical scenario that jobs with different constraints often need to be simultaneously processed. This article addresses a constrained hybrid flow shop scheduling problem (CHFSP) in which certain jobs are bound by a common deadline while other jobs are not. We first propose constructive heuristics to generate initial solutions, completely based on the characteristics of the problem. Additionally, we present a variable-representation discrete artificial bee colony algorithm (VRDABC). The algorithm introduces a novel variable solution representation along with a tailored decoding method, allowing an extensive exploration of the solution space while maintaining a remarkable search speed. By leveraging the distinctive solution representation, we design an insertion operation, a multi-phase evolutionary process, and an efficient phase-transition mechanism, thereby enabling the VRDABC to successfully address this problem. Experimental validations, including algorithmic components analysis and algorithmic comparison, confirm the effectiveness and potential of our approach in producing high-quality solutions. Through the theoretical explanation and experimental analysis, the VRDABC presents an influential contribution to the constrained hybrid flow shop scheduling problem.

A discrete artificial bee colony algorithm and its application in flexible flow shop scheduling with assembly and machine deterioration effect

In real production, assembly and machine deterioration are extremely common phenomenon and flexible flow shop scheduling problem (FFSP) is also extensively investigated. However, assembly FFSP with machine deterioration effect (AFFSP-DE) is neglected. This paper addresses an AFFSP-DE with makespan criterion and proposes a discrete artificial bee colony (DABC) algorithm. In DABC, the number of employed (onlooker) bees is dynamically changeable according to evolutionary quality to adaptively allocate computing resources for two kinds of bees, and a new scout bee phase is designed to improve convergence speed. Finally, the relationships between makespan and different deterioration rates are analyzed systematically by extensive experiments, and the results demonstrate that DABC outperforms the existing algorithms over 82% test instances in solving the AFFSP-DE.

Decomposition-based multi-objective approach for a green hybrid flowshop rescheduling problem with consistent sublots

Rescheduling in a hybrid flowshop holds significant importance in modern industries that face uncertain events. Moreover, real-world manufacturing scenarios often utilise lot streaming to enhance market competitiveness. In light of escalating energy demands and their consequential environmental impacts, contemporary manufacturing companies are placing a heightened emphasis on energy efficiency. This study addressed a green hybrid flowshop rescheduling problem with consistent sublots (GHFRP_CS) in the context of urgent lot insertion. Initially, we establish an optimisation model aimed at minimising the makespan, total energy consumption, and system stability. To tackle this NP-hard multi-objective optimization problem, we develop a constructive heuristic generating promising solutions based on lot split, sequence, and local search rules. Further improvement is achieved through a multi-objective discrete artificial bee colony algorithm (MDABC). MDABC decomposes the problem into sub-problems, initiating solutions with the constructive heuristic and refining them through employed bee, onlooker bee, and scout bee phases. Computational experiments compare MDABC with other multi-objective evolutionary algorithms (MOEAs) on small- and large-scale problems. Results demonstrate MDABC's superiority, achieving fourfold accuracy and efficiency enhancement for small-scale instances and sixfold improvement for large-scale problems at low cost compared to other MOEAs.

基于离散人工蜂群算法的农业机器人丘陵地区农田遍历路径规划

In this study, the discrete artificial bee colony (DABC) algorithm was proposed to plan the path of agricultural robots traversing multiple fields in hilly areas. Based on the basic ABC algorithm as the framework, the path coding method was adopted, and the discrete crossover operator, reverse operator, immune operator, and single/multi-step 2-opt operator were comprehensively used to help hired bees, observing bees, and scout bees to generate new food sources. Finally, the optimized field traversal order and the entrance and exit distribution of each field were obtained. The simulation results showed that compared with the traditional ABC algorithm, the average shortest path of the DABC algorithm proposed in this study was shortened by 1.59%, accompanied by the less iterations contributing to algorithm convergence and good ability to jump out of the local optimal solution. The simulation experiment was carried out using real field data and field operation parameters. The field traversal order and the entrance and exit distribution obtained by the proposed method can effectively reduce the length of the transfer path and its repeatability. This study exhibits superiority and feasibility in the field traversal path planning of agricultural robots in hilly areas, and the trajectory coordinates output by the algorithm can provide a path reference for large-area operations of agricultural machinery drivers or unmanned agricultural machineries.

A novel Discrete Artificial Bee Colony algorithm combined with adaptive filtering to extract Fetal Electrocardiogram signals

The Fetal Electrocardiogram (FECG) signal plays a crucial role in monitoring the health of the fetus, but there are numerous challenges in eliminating the maternal thorax signal and reducing noise interference. This paper proposes a novel objective function that combines a Least Mean Squares (LMS) adaptive filter with a heuristic algorithms to enhance the quality of the extracted FECG signal. To achieve better results, we introduce the Discrete Artificial Bee Colony (DABC) algorithm with a new initialization strategy, a random wavelet basic function strategy, and Gaussian distribution. These improvements enhance global search capabilities and ensure a faster convergence rate. The application of heuristic algorithms can reduce noise signals and provides clearer and more accurate results compared to the traditional LMS filter. Furthermore, the effectiveness of this innovative algorithm is compared with other widely used heuristic algorithms. The experiment results demonstrate that the novel algorithm significantly enhances performance by up to 8% compared to other conventional extraction methods in some indicators.

Multi-Objective Multi-Picking-Robot Task Allocation: Mathematical Model and Discrete Artificial Bee Colony Algorithm

Multi-Objective Multi-Picking-Robot Task Allocation: Mathematical Model and Discrete Artificial Bee Colony Algorithm

A Self-learning Discrete Artificial Bee Colony Algorithm for Energy-efficient Distributed Heterogeneous L-R Fuzzy Welding Shop Scheduling Problem

A Self-learning Discrete Artificial Bee Colony Algorithm for Energy-efficient Distributed Heterogeneous L-R Fuzzy Welding Shop Scheduling Problem

Solving human-robot collaborative mixed-model two-sided assembly line balancing using multi-objective discrete artificial bee colony algorithm

Human-robot collaboration (HRC) is an emerging technology that aims to optimize production efficiency and reduce ergonomic risks by enabling humans and collaborative robots to work together in a shared workspace. This paper investigates HRC mixed-model two-sided assembly line balancing problem, which is a challenging real-world problem in manufacturing systems. The problem is formulated using a mixed-integer programming model with the objectives of minimizing ergonomic risk, energy consumption, and cycle time. To solve the problem efficiently, a multi-objective discrete artificial bee colony algorithm with specialist bees (MDABCSB) is proposed. MDABCSB utilizes a new five-vector encoding and decoding scheme and incorporates two types of neighbor structures for each vector. Additionally, the algorithm introduces specialist bees to search for the optimal solution for each objective, combines a tabu list to help them jump out of local optima, and employs variable neighborhood descent to enhance their exploration capabilities. Furthermore, the algorithm adopts a new temperature-based food sources update mechanism to probabilistically update the food sources of employed bees to reinforce exploitation. By comparing the proposed algorithm with other algorithms, the results demonstrate that MDABCSB performs better in terms of solution performance.

A slack speed-up based discrete artificial bee colony algorithm for resource-constrained operating room scheduling problem

In today’s world, with the rapidly increasing demand for health care and the scarcity of health care resources, it is very important to provide more services under limited conditions. This paper focuses on a resource-constrained operating room scheduling (RCORS) problem with the characteristic of multi-stage. First, a mixed integrated programming model is presented to uncover the impact of no-wait and resources on the problem. Second, a slack speed-up based discrete artificial bee colony (SDABC) algorithm is devised to tackle varying configurations effectively. The solution is represented as a one-dimensional list, from which a complete schedule is derived by a decoding heuristic. Additionally, a speed-up evaluation procedure based on a slack-resource model is developed. In SDABC, an initial population with high quality and diversity is generated based on a problem-specific heuristic. Different strategies are employed in the three stages of SDABC to balance the local optimal, population optimal, and global optimal. A variable neighborhood search is introduced to enhance local exploration capabilities. Finally, we conduct a large number of experiments to verify the performance of the proposed algorithm. Numerical analysis proves that SDABC has significant advantages for solving the RCORS problem.

A Hybrid Discrete Artificial Bee Colony Algorithm Based on Label Similarity for Solving Point-Feature Label Placement Problem

The artificial bee colony algorithm (ABC) is a promising metaheuristic algorithm for continuous optimization problems, but it performs poorly in solving discrete problems. To address this issue, this paper proposes a hybrid discrete artificial bee colony (HDABC) algorithm based on label similarity for the point-feature label placement (PFLP) problem. Firstly, to better adapt to PFLP, we have modified the update mechanism for employed bees and onlooker bees. Employed bees learn the label position of the better individuals, while onlooker bees perform dynamic probability searches using two neighborhood operators. Additionally, the onlooker bees’ selection method selects the most promising solutions based on label similarity, which improves the algorithm’s search capabilities. Finally, the Metropolis acceptance strategy is replaced by the original greedy acceptance strategy to avoid the premature convergence problem. Systematic experiments are conducted to verify the effectiveness of the neighborhood solution generation method, the selection operation based on label similarity, and the Metropolis acceptance strategy in this paper. In addition, experimental comparisons were made at different instances and label densities. The experimental results show that the algorithm proposed in this paper is better or more competitive with the compared algorithm.

Joint scheduling of parallel machines and AGVs with sequence-dependent setup times in a matrix workshop

Transportation systems are often ignored in traditional parallel-machine scheduling problems (PMSP). This study addresses the problem of joint scheduling of parallel machines and AGVs (PMSP-AGV) with sequence-dependent setup times in a matrix manufacturing workshop. The goal is to minimize the makespan from the departure of AGVs to completion of the last job. To achieve this goal, a mixed-integer linear programming model (MILP) is established. An efficient discrete artificial bee colony (DABC) algorithm was proposed with improvement strategies, including a two-stage heuristic for initializing the population, six effective neighborhood operators to explore neighborhood solutions, new algorithm control parameters for deep local exploitation, and a multiple-insertion (MI) method used for re-initialization in the scout bee phase. Many instances from a real factory were used as test benchmarks. Comprehensive analysis of the experimental results showed that the proposed DABC algorithm was significantly superior to the four metaheuristics in the literature.

Contingency-resilient PMU placement using Fuzzy Logic and Discrete Artificial Bee Colony algorithm for comprehensive network observability

In transforming traditional grid systems into intelligent and microgrid-based systems, the Phasor Measurement Units (PMUs) play a prominent role in enhancing monitoring, assessment, control, and protection by estimating voltage and current phasors correctly and quickly. However, since PMUs are not cost-effective, the decision to take the requisite number and their placement is a significant issue and needs to focus on a better approach. This study addresses the solution methodology to compute the optimal locations of a reduced PMU number to have complete network observability. A new hybrid optimization strategy based on the Fuzzy Logic (FL) and Discrete Artificial Bee Colony (DABC) algorithm has been suggested. The suggested method is titled FL-DABC algorithm, which has been applied to determining the optimal placement of PMUs in microgrid-based systems. The suggested approach tackles the problem of fully network-observable cost-effective PMU deployment. The effectiveness of the Algorithm is evaluated through simulations under various operating limitations, such as zero injection buses, a single PMU, and line outage scenarios. A topological observability analysis validates full network observability with the computed PMU placement. The study also includes branch PMU placement set computation and a comparative analysis with other techniques using standard IEEE test networks for simulations.

An efficient discrete artificial bee colony algorithm with dynamic calculation method for solving the AGV scheduling problem of delivery and pickup

To meet the production demand of workshop, this paper proposes an efficient discrete artificial bee colony (DABC) algorithm to solve a new automatic guided vehicle (AGV) scheduling problem with delivery and pickup in a matrix manufacturing workshop. The goal is to produce a AGV transportation solution that minimizes the total cost, including travel cost, time cost, and AGV cost. Therefore, a mixed integer linear programming model is established. To improve the transportation efficiency, a dynamic calculation method is developed. In the DABC algorithm, a heuristic algorithm and a median based probability selection method are used. For improving the quality of the solutions, four effective neighborhood operators are introduced. In the local search, a rule is given to save the operation time and a problem-based search operator is proposed to improve the quality of the best individual. Finally, a series of comparison experiments were implemented with the iterative greedy algorithm, artificial bee colony algorithm, hybrid fruit fly optimization algorithm, discrete artificial bee colony algorithm, improved harmony search, and hybrid genetic-sweep algorithm. The results show that the proposed DABC algorithm has high performance on solving the delivery and pickup problem.

Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms

Healthcare services become increasingly technology dependent every passing day such as the Internet of Things (IoT), Fog Computing, 5th generation (5G) and beyond communications, etc. They enable the processing and exchange of huge volumes of healthcare data whose integrity and real-time delivery are critical for healthcare services. Optimal power consumption in such essential healthcare infrastructure is critical for the well-being of patients and crucial to reduce the operational cost of healthcare facilities. In this paper, a Fog node has been introduced in an IoT healthcare infrastructure with power consumption as a key deciding factor. This work proposed a mathematical formulation to decide the deployment of two heterogeneous gateways in the healthcare infrastructure. The target of optimization is to minimize transmission power and infrastructure costs. Two swarm intelligence-based algorithms have been used to solve the computationally challenging optimization problem. These evolutionary algorithms are a discrete fireworks algorithm and a discrete artificial bee colony algorithm with an ensemble of local search methods. Their performance is compared against the genetic algorithm. The simulation results demonstrate a saving of up to 33 percent in power consumption in the proposed healthcare infrastructure that can significantly improve healthcare data communications and its operational costs.

Large-scale emergency medical services scheduling during the outbreak of epidemics.

This paper studies a new large-scale emergency medical services scheduling (EMSS) problem during the outbreak of epidemics like COVID-19, which aims to determine an optimal scheduling scheme of emergency medical services to minimize the completion time of nucleic acid testing to achieve rapid epidemic interruption. We first analyze the impact of the epidemic spread and assign different priorities to different emergency medical services demand points according to the degree of urgency. Then, we formulate the EMSS as a mixed-integer linear program (MILP) model and analyze its complexity. Given the NP-hardness of the problem, we develop two fast and effective improved discrete artificial bee colony algorithms (IDABC) based on problem properties. Experimental results for a real case and practical-sized instances with up to 100 demand points demonstrate that the IDABC significantly outperforms MILP solver CPLEX and two state-of-the-art metaheuristic algorithms in both solution quality and computational efficiency. In addition, we also propose some managerial implications to support emergency management decision-making.

Design of agricultural product cold chain transportation monitoring system based on Internet of Things technology

The cold chain transportation monitoring system is mainly responsible for data monitoring and control in the cold chain transportation environment, ensuring the stability of the cold chain compartment environment, and playing an important role in the transportation and storage of fresh agricultural products. The traditional Internet of Things cold chain control system uses manual input of control parameters to realize the monitoring of the cold chain compartment environment, which lacks intelligence. Therefore, the BP (Back propagation, BP) neural network is used to build a cold chain transportation monitoring model to realize the dynamic monitoring of the cold chain compartment environment. Considering that the BP network is easy to fall into the problem of “local fall”, which leads to the decline of the prediction accuracy of the model, the improved discrete artificial bee colony algorithm is used to optimize the parameters of the BP neural network, and the fish swarm algorithm is used to decode the discrete population to obtain the final result. Decimal value to construct the DABC-BP cold chain transportation control model. The experimental simulation results show that in the $$f_{1} (x)$$ function optimization test, the improved DABC-BP (Discrete Artificial Bee Colony-Back Propagation Neural Network, DABC-BP) algorithm has the best optimization accuracy, and the minimum logarithm after 1000 iterations is 10−7. Meanwhile, the DABC algorithm has the best convergence performance, reaching convergence after 821 iterations. Compared with related hybrid algorithms such as mayfly algorithm and extreme learning machine, this research method is suitable for harsher environments and more sensitive to temperature monitoring. The research has important reference value for the transportation and storage of modern agricultural products.

A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots

Hybrid flowshop scheduling problems are encountered in many real-world manufacturing scenarios. With increasingly fierce market competition, the production mode of multiple varieties and small batches has gradually been accepted by enterprises, where the technology of lot streaming is widely used. Meanwhile, green criteria, such as energy consumption and carbon emissions, have attracted increasing attention to improving protection awareness. With these motivations, this paper studies a multiobjective hybrid flowshop green scheduling problem with consistent sublots (MOHFGSP_CS), aiming to minimize two objectives, i.e., makespan and total energy consumption, simultaneously. To solve this complex problem, we first formulate a novel multiobjective optimization model. However, due to the NP-hard nature of the problem, the model is computationally prohibitive as the problem scale increases. Thus, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is proposed. There are three phases in this algorithm: the VND-based employed bee phase, the adjustment weight onlooker bee phase, and the population interaction scout bee phase. In the experimental study, various small-scale and large-scale instances are collected to verify the effectiveness of the multiobjective optimization model and the MDABC. Comprehensive computational comparisons and statistical analysis show that the developed strategies and MDABC show superior performance.

A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

Imaging satellite mission planning has received more and more attention as one of the core problems in the field of imaging satellite applications. In this paper, a hybrid discrete artificial bee colony (HDABC) algorithm is proposed to address this problem. The HDABC algorithm improves the three search phases of the basic artificial bee colony (ABC) algorithm to make them applicable to the discrete satellite mission planning problem. In the employed bee search phase, the population is divided and a multi-strategy search equation mechanism is used to balance the exploration and development of the algorithm. In the following bee search phase, two kinds of neighborhood search operators are designed based on the problem characteristics to further improve the fitness values of the better solutions. In the scout bee search phase, a migration operator and an immigration operator are introduced to improve the fitness values of the worse solutions and promote the exchange of different subpopulations to achieve co-evolution. In the experimental part, orthogonal experimental design is used to determine the appropriate algorithm parameters. Simulation experiments are carried out to test problems of different sizes. The experimental results show that the proposed HDABC algorithm shows good performance.

Discrete Artificial Bee Colony Algorithm for Multi-objective Distributed Heterogeneous No-wait Flowshop Scheduling Problem

Discrete Artificial Bee Colony Algorithm for Multi-objective Distributed Heterogeneous No-wait Flowshop Scheduling Problem