A new beetle antennae search algorithm for multi-objective energy management in microgrid

Zhu, Zongyao; Zhang, Zhiyu; Man, Weishi; Tong, Xiangqian; Qiu, Jinzhe; Li, Fangfang

doi:10.1109/iciea.2018.8397965

Cited by 65 publications

(33 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimation of an approximated gradient is a key feature of BAS, which distinguishes it from another metaheuristic algorithm. Since its introduction, BAS has found its application in several real-world systems [17], [55]- [64]. The working of the original BAS can be described like this; at each iteration, the value of the objective function is computed at each antennae fiber location, a vector is drawn from the fiber with the lowest value toward the fiber with the highest value, the vector represents the direction of the approximated gradient.…”

Section: (A)mentioning

confidence: 99%

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

Khan

Cao

et al. 2020

IEEE/CAA J. Autom. Sinica

168

View full text Add to dashboard Cite

In this paper, we propose enhancements to Beetle Antennae Search (BAS) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the Adaptive Moment Estimation (ADAM) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with Particle Swarm Optimizer (PSO) and the original BAS algorithm.

show abstract

Section: (A)mentioning

confidence: 99%

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

Khan

Cao

et al. 2020

IEEE/CAA J. Autom. Sinica

168

View full text Add to dashboard Cite

show abstract

“…As shown in Figure , the algorithm of this paper tends to converge at about the 20th generation, and the final value of function can reach about 3.5. While the beetle antennae search (BAS) algorithm only reaches convergence in the 30th generation, its value can only reach 5.5. The global optimal solution cannot be obtained with the BAS (some parameters of BAS are shown in Table ).…”

Section: Simulationmentioning

confidence: 99%

“…where we specify r i,i = +∞ because the transmission delay of the cluster-head vehicle V k,i to itself is 0. Then, according to Equations (20) and (24), the system consumption function at this time can be expressed as…”

Section: Problem Conversionmentioning

confidence: 99%

Joint optimization of computation cost and delay for task offloading in vehicular fog networks

Wang

2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The vehicles equipped with computing devices can provide computing services in vehicular fog networks, thereby reducing latency and improving system efficiency. Hence, we face with the problem of how to select vehicles for the computation offloading of the task. In this paper, we jointly consider the delay and cost, and study the resource allocation problem of multivehicles users. First of all, we construct a hierarchical offloading model, use mathematical method to construct consumption function of the system, and generate a convex optimization problem with inequality constraints. Then, we use Lagrange method to solve the problem and develop a low‐complexity algorithm to select vehicles, optimize the offloading ratio, and reduce the consumption of the system. Numerical results show that the algorithm has a fast convergence rate and good performance.

show abstract

“…Our proposed algorithm is based on a nature-inspired metaheuristic optimization algorithm; Beetle Antennae Olfactory (BAO) algorithm [33], [34], inspired by the food searching behavior of beetles. Although recently introduced, BAO has shown practical applications in several real-world scenarios [35], [36] and therefore, the reason for our choice for solving the formulated optimization problem. Specifically, The formulation of the BAO algorithm allows the use of the "virtual robots", which virtually anticipate the consequences of joint-actions and only move the real robot when accuracy and collision-safety are guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

Khan

Luo

2020

IEEE Trans. Ind. Inf.

194

View full text Add to dashboard Cite

A new beetle antennae search algorithm for multi-objective energy management in microgrid

Cited by 65 publications

References 6 publications

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

Joint optimization of computation cost and delay for task offloading in vehicular fog networks

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

Contact Info

Product

Resources

About