## What is/are Discrete Artificial?

Discrete Artificial - Further comparisons are made using standard boundary conditions like, Dirichlet, Neumann and a variant of absorbing boundary conditions called discrete artificial ones.^{[1]}Traditional precision measurement adopts discrete artificial static observation, which cannot meet the demands of the dynamic, continuous, fine and high-precision holographic measurement of large-scale infrastructure construction and complex operation and maintenance management.

^{[2]}The efficiency of the proposed model is tested on the real transmission network of Azerbaijan regional electric company using a discrete artificial bee colony (DABC) and quadratic programming (QP) based method.

^{[3]}28% when compared against discrete artificial bee colony with 3 LSM (DABC-3-LSM), low-complexity biogeography-based optimization (LC-BBO), and genetic algorithm, respectively.

^{[4]}To effectively solve this DHNWFSP, a discrete artificial bee colony algorithm (DABC) is proposed.

^{[5]}Nondominated sorting genetic algorithm II, bi-objective multi-start simulated annealing method, and hybrid multi-objective discrete artificial bee colony are chosen for comparisons.

^{[6]}Based on the discrete artificial boundary condition introduced in [23] for the two-dimensional free Schrodinger equation in a computational rectangular domain, we propose to analyze the stability and convergence rate of the resulting full scheme.

^{[7]}To ensure the optimal classification performance, this paper proposes a hybrid feature selection algorithm based on an improved discrete artificial bee colony algorithm to improve the efficiency of feature selection.

^{[8]}In this paper, the discrete Artificial Bee Colony (dABC SPARQL ) algorithm is proposed, based on a novel heuristic approach, namely reordering SPARQL queries.

^{[9]}For medium-scale instances, a discrete artificial bee colony algorithm with hybrid neighborhood search mechanism is introduced to capture high-quality solutions.

^{[10]}For the purpose, we propose three constructive heuristics and an effective discrete artificial bee colony (DABC) algorithm.

^{[11]}The dynamic tensile response of additively manufactured Ti6Al4V shear-tension specimens containing discrete artificial pores was investigated under quasi-static and dynamic loading.

^{[12]}The test results are compared with other metaheuristic methods like Discrete Artificial Bee Colony algorithm (DABC) and Particle Swam Optimization (PSO) algorithm.

^{[13]}In this study, the flexible task scheduling problem in a cloud computing system is studied and solved by a hybrid discrete artificial bee colony (ABC) algorithm, where the considered problem is firstly modeled as a hybrid flowshop scheduling (HFS) problem.

^{[14]}In this research, an effective discrete artificial fish swarm algorithm is developed to solve the cost-oriented assembly line balancing problems which aims to minimize the construction cost and at the same time minimize the number of mate-station.

^{[15]}For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed.

^{[16]}The metaheuristics are based on the high-performing frameworks of discrete artificial bee colony, scatter search, iterated local search, and iterated greedy, which have been applied with great success to closely related scheduling problems.

^{[17]}We also compare the performance of the proposed optimization algorithm with classical and state-of-the-art population-based optimizers, namely, Genetic Algorithm, Binary Hybrid Topology Particle Swarm Optimization, Selectively Informed Particle Swarm Optimization, Binary Learning Differential Evolution, and Discrete Artificial Bee Colony.

^{[18]}In this paper, a mathematical model and an improved discrete artificial bee colony algorithm (DABC) are proposed for the WSSP.

^{[19]}An improved discrete artificial bee colony (DABC) algorithm for minimizing makespan is proposed as well.

^{[20]}To solve this complex problem, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is suggested.

^{[21]}Then, an adaptive discrete artificial bee colony algorithm (ADABC) is devised to resolve the TLCIE model efficiently.

^{[22]}Based on the idea of chaotic mapping and reverse learning, the food source coding and initialization were redesigned, and a modified discrete artificial bee colony algorithm was constructed to make joint decision on part supply time and delivery quantity.

^{[23]}A hybrid method based on modified discrete artificial bee colony algorithm(MDABC) for power quality disturbance(PQD) signal feature selection and parameter optimization of random forest(RF) is proposed.

^{[24]}So, an improved discrete artificial bee colony algorithm is proposed.

^{[25]}The results show that the Multi-objective Discrete Artificial Bee Colony algorithm has great advantages in solving the routing problem of grain transportation vehicles with time windows.

^{[26]}This paper shows the use of Discrete Artificial Bee Colony (DABC) and Particle Swarm Optimization (PSO) algorithm for solving the job shop scheduling problem (JSSP) with the objective of minimizing makespan.

^{[27]}To address the issue, Improved Discrete Artificial Fish swarm algorithm combined with Margin distance minimization for Ensemble Pruning (IDAFMEP) is proposed using a combination of diversity measure and heuristic algorithm.

^{[28]}In view of the complicated flow mechanism of shale gas flow, multi-scale media and multiple pressure systems of organic matter/matrix pore/natural fractures and discrete artificial fractures are established to accurately describe the gas flow in shale reservoir.

^{[29]}Since the problem is NP-complete, an energy-efficient discrete artificial bee colony (DABC), and an energy-efficient genetic algorithm (MOGA), also a variant of this algorithm (MOGALS) are developed, as heuristic methods.

^{[30]}The paper introduces a path planning method for an autonomous mobile robot, called the Discrete Artificial Potential Field algorithm (DAPF).

^{[31]}

## bee colony algorithm

To effectively solve this DHNWFSP, a discrete artificial bee colony algorithm (DABC) is proposed.^{[1]}To ensure the optimal classification performance, this paper proposes a hybrid feature selection algorithm based on an improved discrete artificial bee colony algorithm to improve the efficiency of feature selection.

^{[2]}For medium-scale instances, a discrete artificial bee colony algorithm with hybrid neighborhood search mechanism is introduced to capture high-quality solutions.

^{[3]}The test results are compared with other metaheuristic methods like Discrete Artificial Bee Colony algorithm (DABC) and Particle Swam Optimization (PSO) algorithm.

^{[4]}For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed.

^{[5]}In this paper, a mathematical model and an improved discrete artificial bee colony algorithm (DABC) are proposed for the WSSP.

^{[6]}To solve this complex problem, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is suggested.

^{[7]}Then, an adaptive discrete artificial bee colony algorithm (ADABC) is devised to resolve the TLCIE model efficiently.

^{[8]}Based on the idea of chaotic mapping and reverse learning, the food source coding and initialization were redesigned, and a modified discrete artificial bee colony algorithm was constructed to make joint decision on part supply time and delivery quantity.

^{[9]}So, an improved discrete artificial bee colony algorithm is proposed.

^{[10]}The results show that the Multi-objective Discrete Artificial Bee Colony algorithm has great advantages in solving the routing problem of grain transportation vehicles with time windows.

^{[11]}

## Improved Discrete Artificial

To ensure the optimal classification performance, this paper proposes a hybrid feature selection algorithm based on an improved discrete artificial bee colony algorithm to improve the efficiency of feature selection.^{[1]}In this paper, a mathematical model and an improved discrete artificial bee colony algorithm (DABC) are proposed for the WSSP.

^{[2]}An improved discrete artificial bee colony (DABC) algorithm for minimizing makespan is proposed as well.

^{[3]}So, an improved discrete artificial bee colony algorithm is proposed.

^{[4]}To address the issue, Improved Discrete Artificial Fish swarm algorithm combined with Margin distance minimization for Ensemble Pruning (IDAFMEP) is proposed using a combination of diversity measure and heuristic algorithm.

^{[5]}

## Effective Discrete Artificial

For the purpose, we propose three constructive heuristics and an effective discrete artificial bee colony (DABC) algorithm.^{[1]}In this research, an effective discrete artificial fish swarm algorithm is developed to solve the cost-oriented assembly line balancing problems which aims to minimize the construction cost and at the same time minimize the number of mate-station.

^{[2]}

## Modified Discrete Artificial

Based on the idea of chaotic mapping and reverse learning, the food source coding and initialization were redesigned, and a modified discrete artificial bee colony algorithm was constructed to make joint decision on part supply time and delivery quantity.^{[1]}A hybrid method based on modified discrete artificial bee colony algorithm(MDABC) for power quality disturbance(PQD) signal feature selection and parameter optimization of random forest(RF) is proposed.

^{[2]}

## Objective Discrete Artificial

Nondominated sorting genetic algorithm II, bi-objective multi-start simulated annealing method, and hybrid multi-objective discrete artificial bee colony are chosen for comparisons.^{[1]}The results show that the Multi-objective Discrete Artificial Bee Colony algorithm has great advantages in solving the routing problem of grain transportation vehicles with time windows.

^{[2]}

## discrete artificial bee

The efficiency of the proposed model is tested on the real transmission network of Azerbaijan regional electric company using a discrete artificial bee colony (DABC) and quadratic programming (QP) based method.^{[1]}28% when compared against discrete artificial bee colony with 3 LSM (DABC-3-LSM), low-complexity biogeography-based optimization (LC-BBO), and genetic algorithm, respectively.

^{[2]}To effectively solve this DHNWFSP, a discrete artificial bee colony algorithm (DABC) is proposed.

^{[3]}Nondominated sorting genetic algorithm II, bi-objective multi-start simulated annealing method, and hybrid multi-objective discrete artificial bee colony are chosen for comparisons.

^{[4]}To ensure the optimal classification performance, this paper proposes a hybrid feature selection algorithm based on an improved discrete artificial bee colony algorithm to improve the efficiency of feature selection.

^{[5]}In this paper, the discrete Artificial Bee Colony (dABC SPARQL ) algorithm is proposed, based on a novel heuristic approach, namely reordering SPARQL queries.

^{[6]}For medium-scale instances, a discrete artificial bee colony algorithm with hybrid neighborhood search mechanism is introduced to capture high-quality solutions.

^{[7]}For the purpose, we propose three constructive heuristics and an effective discrete artificial bee colony (DABC) algorithm.

^{[8]}The test results are compared with other metaheuristic methods like Discrete Artificial Bee Colony algorithm (DABC) and Particle Swam Optimization (PSO) algorithm.

^{[9]}In this study, the flexible task scheduling problem in a cloud computing system is studied and solved by a hybrid discrete artificial bee colony (ABC) algorithm, where the considered problem is firstly modeled as a hybrid flowshop scheduling (HFS) problem.

^{[10]}For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed.

^{[11]}The metaheuristics are based on the high-performing frameworks of discrete artificial bee colony, scatter search, iterated local search, and iterated greedy, which have been applied with great success to closely related scheduling problems.

^{[12]}We also compare the performance of the proposed optimization algorithm with classical and state-of-the-art population-based optimizers, namely, Genetic Algorithm, Binary Hybrid Topology Particle Swarm Optimization, Selectively Informed Particle Swarm Optimization, Binary Learning Differential Evolution, and Discrete Artificial Bee Colony.

^{[13]}In this paper, a mathematical model and an improved discrete artificial bee colony algorithm (DABC) are proposed for the WSSP.

^{[14]}An improved discrete artificial bee colony (DABC) algorithm for minimizing makespan is proposed as well.

^{[15]}To solve this complex problem, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is suggested.

^{[16]}Then, an adaptive discrete artificial bee colony algorithm (ADABC) is devised to resolve the TLCIE model efficiently.

^{[17]}Based on the idea of chaotic mapping and reverse learning, the food source coding and initialization were redesigned, and a modified discrete artificial bee colony algorithm was constructed to make joint decision on part supply time and delivery quantity.

^{[18]}A hybrid method based on modified discrete artificial bee colony algorithm(MDABC) for power quality disturbance(PQD) signal feature selection and parameter optimization of random forest(RF) is proposed.

^{[19]}So, an improved discrete artificial bee colony algorithm is proposed.

^{[20]}The results show that the Multi-objective Discrete Artificial Bee Colony algorithm has great advantages in solving the routing problem of grain transportation vehicles with time windows.

^{[21]}This paper shows the use of Discrete Artificial Bee Colony (DABC) and Particle Swarm Optimization (PSO) algorithm for solving the job shop scheduling problem (JSSP) with the objective of minimizing makespan.

^{[22]}Since the problem is NP-complete, an energy-efficient discrete artificial bee colony (DABC), and an energy-efficient genetic algorithm (MOGA), also a variant of this algorithm (MOGALS) are developed, as heuristic methods.

^{[23]}

## discrete artificial fish

In this research, an effective discrete artificial fish swarm algorithm is developed to solve the cost-oriented assembly line balancing problems which aims to minimize the construction cost and at the same time minimize the number of mate-station.^{[1]}To address the issue, Improved Discrete Artificial Fish swarm algorithm combined with Margin distance minimization for Ensemble Pruning (IDAFMEP) is proposed using a combination of diversity measure and heuristic algorithm.

^{[2]}