In many non-deterministic search algorithms, particularly those analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. The BA is a population-based metaheuristic search algor...
Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have re...
Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected p...
In this study the problem of fitting shape primitives to point cloud scenes was tackled as a parameter optimisation procedure, and solved using the popular bees algorithm. Tested on three sets of clean and differently blurred point cloud models, the...
Improving the performance of Deep Learning (DL) algorithms is a challenging problem. However, DL is applied to different types of Deep Neural Networks, and Long Short-Term Memory (LSTM) is one of them that deals with time series or sequential data. T...
One of the essential parts of contemporary cryptographic systems is s-boxes (Substitution Boxes), which give encryption algorithms more complexity and resilience due to their nonlinear structure. In this study, we propose CSBA (Cuckoo Search–Be...
Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a nov...
Online education has gained increasing importance in recent years due to its flexibility and ability to cater to a diverse range of learners. The COVID-19 pandemic has further emphasized the significance of online education as a means to ensure conti...
This paper focuses on using the Bees Algorithm (BA) to tune the parameters of the proposed Fuzzy Proportional–Integral–Derivative with Filtered derivative (Fuzzy PIDF), Fractional Order PID (FOPID) controller and classical PID controller developed to...
In the context of intelligent manufacturing, equipment fault early-warning technology has become a critical support for ensuring the continuity and safety of industrial production. However, with the increasing complexity of modern industrial equipmen...
As resources become increasingly scarce and environmental demands grow, the recycling of products at the end of their lifecycle becomes crucial. Disassembly, as a key stage in the recycling process, plays a decisive role in the sustainability of the...
This paper proposes a design of Sliding Mode Control (SMC) for Load Frequency Control (LFC) in a two-area electrical power system. The mathematical model design of the SMC is derived based on the parameters of the investigated system. In order to ach...
As the number of end-of-life products multiplies, the issue of their efficient disassembly has become a critical problem that urgently needs addressing. The field of disassembly sequence planning has consequently attracted considerable attention. In...
This study develops and implements a design of the Fuzzy Proportional Integral Derivative with filtered derivative mode (Fuzzy PIDF) for Load Frequency Control (LFC) of a two-area interconnected power system. To attain the optimal values of the propo...
Remanufacturing, which benefits the environment and saves resources, is attracting increasing attention. Disassembly is arguably the most critical step in the remanufacturing of end-of-life (EoL) products. Human–robot collaborative disassembly...
To address pronounced fluctuations in monitored parameters and the difficulty of timely and accurate detection of incipient anomalies in hydropower units, this paper proposes a fault early warning method based on an improved Bees Algorithm with Two P...
Aiming to address the complex degenerate primer design problem in the biological field, in this paper, we design a degenerate primer optimization model considering primer coverage and degeneracy that allows a small number of base mismatches, and prop...
The Artificial Bee Colony (ABC) algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept that is presented by nature language and its quantitative e...
Artificial neural networks (ANNs), one of the most important artificial intelligence techniques, are used extensively in modeling many types of problems. A successful training process is required to create effective models with ANN. An effective trai...
In this study, a neural network-based approach is proposed for the identification of nonlinear static systems. A variant called ABCES (ABC Based on Effective Scout Bee Stage) is introduced for neural network training. Two important changes are carrie...
The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee col...
The nurse scheduling problem (NSP) is an NP-Hard combinatorial optimization scheduling problem that allocates a set of shifts to the group of nurses concerning the schedule period subject to the constraints. The objective of the NSP is to create a sc...
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 co...
The artificial bee colony algorithm (ABC), as a classic swarm intelligence algorithm, has advantages such as fewer parameters and clear logic. However, ABC cannot balance the exploration and development stages well in the iterative process, and is ea...
The Artificial Bee Colony (ABC) algorithm is widely used for continuous optimization, but the standard ABC still suffers from insufficient use of neighborhood information, limited adaptability of search behavior, and random restarts in the scout bee...
To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable...
As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In...
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...
K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee c...
Parameter identification is an important research topic with a variety of applications in industrial and environmental problems. Usually, a functional has to be minimized in conjunction with parameter identification; thus, there is a certain similari...
Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are sto...
Modern day industries strive to obtain long-term supplier integrations (SI) with potentially stronger supplier groups, to achieve fast and reliable production. This paper studies the process of selecting vendors, while simultaneously considering the...
Due to the rapid increase in cargoes and postal transport volumes in smart transportation systems, an efficient automated multidimensional terminal with autonomous elevating transfer vehicles (ETVs) should be established, and an effective cooperative...
Pipeline corrosion prediction (PCP) is an important technology for pipeline maintenance and management. How to accurately predict pipeline corrosion is a challenging task. To address the drawback of the poor prediction accuracy of the grey model (GM(...
Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. Thi...
Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the iss...
Abrasive air jet machining is a burgeoning non-traditional machining technology particularly suitable for machining brittle non-metallic materials and metals with high hardness. It is very challenging to select the optimal process parameters to achie...
Optimal power flow (OPF) objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper,...
In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, g...
Color image quantization techniques have been widely used as an important approach in color image processing and data compression. The key to color image quantization is a good color palette. A new method for color image quantization is proposed in t...
This study presents a novel hybrid Bat-Artificial Bee Colony (BA-ABC) algorithm for energy-efficient optimization in Wireless Sensor Networks (WSNs), addressing the critical challenge of limited node energy and network lifetime degradation. The propo...
Mixed pixels are common in hyperspectral remote sensing images. Endmember extraction is a key step in spectral unmixing. The linear spectral mixture model (LSMM) constitutes a geometric approach that is commonly used for this purpose. This paper intr...
Software defects are problems in software that destroy normal operation ability and reflect the quality of the software. Software fault can be predicted by the software reliability model. In this paper, the hybrid algorithm is applied to parameter es...
This paper proposes an improved artificial bee colony algorithm named Weighted Global ABC (WGABC) algorithm, which is designed to improve the convergence speed in the search stage of solution search equation. The new method not only considers the eff...
The Capacitated Arc Routing Problem (CARP) is a combinatorial optimization problem, which requires the identification of such route plans on a given graph to a number of vehicles that generates the least total cost. The Dynamic CARP (DCARP) is a vari...
The microgrid has two main steady-state modes: grid-connected mode and islanded mode. The microgrid needs a high-performance controller to reduce the overshoot value that affects the efficiency of the network. However, the high voltage value causes t...
Artificial bee colony (ABC) algorithm, a novel category of bionic intelligent optimization algorithm, was achieved for solving complex nonlinear optimization problems. Previous studies have shown that ABC algorithm is competitive to other biological-...
The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cann...
As smart homes and Internet of Things (IoT) ecosystems continue to expand, the need for energy-efficient and low-latency communication has become increasingly critical. One of the key challenges in these systems is minimizing delay time while ensurin...
In the last decades, the availability constraint as well as learning and deteriorating effects were introduced into the production scheduling theory to simulate real-world case studies and to overcome the limitation of the classical models. To the be...
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