Abstract In order to improve the revenue of attacking mining pools and miners under block withholding attack, we propose miner optimization algorithm (MROA) based on Pareto artificial bee colony in blockchain network. MROA establishes model each pool entire attack with mathematical formulas such as selection, effective computing power, cost revenue. Then, solves by using modified method. Namely...

Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it exploration capacity over exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, enhance performance algorithm, novel bas...

This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm pre...

Artificial bee colony (ABC) is a swarm optimization algorithmwhich has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant interest from researchers studying in different fields because of having fewer control parameters, ...

A wide range of engineering design problems have been solved by the algorithms that simulates collective intelligence in swarms of birds or insects. The Artificial Bee Colony or ABC is one of the recent additions to the class of swarm intelligence based algorithms that mimics the foraging behavior of honey bees. ABC consists of three groups of bees namely employed, onlooker and scout bees. In A...

Artificial neural networks (ANNs), one of the most important artificial intelligence techniques, are used extensively in modeling many types problems. A successful training process is required to create effective models with ANN. An algorithm essential for a process. In this study, new network called hybrid bee colony based on scout stage (HABCES) was proposed. The HABCES includes four fundamen...

Green manufacturing, which takes environmental effect and production benefit into consideration, has attracted increasing concern with the target of carbon peaking neutrality proposed. As a critical process in manufacturing system, shop scheduling is also an important method for enterprises to achieve green manufacturing. Therefore, it necessary consider both benefits objectives scheduling, are...

There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance extension execution time. To tackle this problem, feature selection techniques used screen out features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic with high exploration low exploitation capacities. balance between both capa...

The Artificial Bee Colony (ABC) algorithm is inspired by the behavior of honey bees. The algorithm is one of the Swarm Intelligence algorithms explored in recent literature. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, ABC can sometimes be slow to converge. In order to improve the algorithm performance, we present a modified ...

Quantitative Structure–Activity Relationship (QSAR) aims to correlate molecular structure properties with corresponding bioactivity. Chance correlations and multicollinearity are two major problems often encountered when generating QSAR models. Feature selection can significantly improve the accuracy interpretability of by removing redundant or irrelevant descriptors. An artificial bee colony a...