An Improved Hybrid Method Combining Gravitational Search Algorithm With Dynamic Multi Swarm Particle Swarm Optimization

The bi-directional linkage between the power grid and electric vehicles (EVs) enables flexible, cheap and fast-responding use of vehicle batteries in the grid. However, the battery aging effects due to the additional operation cycles caused by Vehicle-to-Grid (V2G) service and the concern of the battery degradation are the main reason that keeps the customer from being the named prosumer of the grid. This paper proposes a novel active battery anti-aging V2G scheduling approach. Firstly, to evaluate the battery aging effect in V2G service, the battery degradation phenomenon is quantified by a novel use of rain-flow cycle counting (RCC) algorithm. Then, the V2G scheduling is modeled as a multi-objective optimization problem, in which the minimal battery degradation and grid load fluctuation are designed as the optimization objectives. Finally, a multi-population collaborative mechanism, which is particularly designed for the V2G scheduling problem, is also developed to improve the practicability and performance of the heuristic optimization based V2G scheduling method. The proposed methodologies are verified by numerical analysis, which highlights that the proposed V2G scheduling method can minimize battery charge/discharge cycles by optimizing the time and scale of each V2G participant while providing the same services to the grid as expected. INDEX TERMS Electric vehicle, vehicle to grid, active battery anti-aging and heuristic algorithm.

Section: B the Heuristic Algorithm Based V2g Scheduling Methodsmentioning

confidence: 99%

Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

2020

“…VI-A, the same environment (feature, video sequence and parameter) is selected. In addition, population size and the iteration number are investigated by dividing the range [10,50] and [20,100] into equal parts, with each a space 10 and 20, respectively. Other parameters are fixed (i.e., l = 1.5, f = 0.5, k = 0.25).…”

Section: B Comparison Of Tracking Performance Among Tlgoa and Agoamentioning

confidence: 99%

“…Hybridizing optimization algorithm is the latest research trend for overcoming the poor exploration ability of one algorithm and poor exploitation ability of the other algorithm. There are many hybrid optimization algorithms proposed such as Genetic Learning PSO (GL-PSO) [19], hybrid Gravitational Search Algorithm with Dynamic Multi swarm PSO (GSADMSPSO) [20], hybrid Whale Optimization Algorithm with Simulated Annealing (WOA-SA) [21], hybrid Bat Algorithm with Harmony Search (BHS) [22], hybrid Cuckoo Search(HCS) [23], Sine Cosine Water Wave Optimization (SCWWO) [24], etc. These literatures show that hybrid optimization algorithms are designed and utilized for complex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Hybridizing Teaching-Learning-Based Optimization With Adaptive Grasshopper Optimization Algorithm for Abrupt Motion Tracking

Zhang

Gao

et al. 2019

Aiming at the problem that conventional tracking algorithms are difficult to deal with abrupt motion efficiently, an optimization algorithm called hybrid Teaching-learning-based optimization with Adaptive Grasshopper Optimization Algorithm (TLGOA) is proposed in this paper. Firstly, the non-linear strategy based on tangent function is used to replace the linear mechanism in the standard Grasshopper Optimization Algorithm (GOA). The improved adaptive GOA (AGOA) can avoid the local trapping problem and enhance the global optimization ability, which can handle the problem of abrupt motion. Secondly, considering that Teaching-learning-based optimization (TLBO) has obviously local exploitation operator and fast convergence, a hybrid TLGOA tracker is designed by combining the advantages of both AGOA and TLBO. The approach can enable better tracking accuracy and efficiency. Finally, extensive experimental results show that the proposed algorithm has obvious advantages over other algorithms, and also prove that TLGOA tracker is very competitive compared to other state-of-the-art trackers, especially for abrupt motion tracking. INDEX TERMS Visual tracking, abrupt motion, grasshopper optimization algorithm, teaching-learningbased optimization.

“…In [36], PSO was used to improve the search ability of GSA. In [37], dynamic multi-swarm PSO and GSA were hybridized to enhance the performance of algorithm. In [38], an opposition-based learning method was combined with GSA.…”

Section: Introductionmentioning

confidence: 99%

A Gravitational Search Algorithm With Chaotic Neural Oscillators

Wang

Gao

et al. 2020

Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.