A circle chaos random search strategy particle swarm optimization with its application

Xiang, Xi; Yan, Xiaoming; Gao, Chuyi; Zhu, Shuliang; Xi, Maolong; Gao, Hao

doi:10.1016/j.compeleceng.2022.108219

Cited by 3 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the group SI optimization algorithms, both convergence acceleration and search efficiency are directly affected by swarm diversity. Some scholars prefer chaotic mapping to initialize the swarm to promote diversity [45], given that initializing the swarm with the chaotic algorithm helps it jump out of the local optima traps. However, this way has a disadvantage due to the close contact with the proximity and the high randomness and uncertainty faced, which impedes equilibrium if the iteration encounters an unstable point.…”

Section: E Opposition-based Learningmentioning

confidence: 99%

Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing

Gad

Houssein

Zhou

et al. 2024

IEEE Internet Things J.

View full text Add to dashboard Cite

In recent years, ongoing advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes of data, taxing the capabilities of cloud computing infrastructure. Allocating limited computing resources to numerous incoming requests is one of the difficulties of cloud computing, which is typically referred to as a Task-Scheduling-in-Cloud-Computing (TSCC) problem. In order to ameliorate the performance of a particle swarm optimizer (PSO) and broaden its application to TSCC, this paper introduces an Opposition-based Simulated Annealing Particle Swarm Optimizer (OSAPSO) to address PSO's premature convergence issue, particularly when tackling high-dimensional complex problems like TSCC. OSAPSO is a novel combination of opposition-based learning (OBL), evolution strategy, simulated annealing (SA), and swarm intelligence. At its initial stage, the swarm is formed at random by using OBL to guarantee swarm diversity with a light computational burden. A multi-way tournament selection approach is then utilized to pick parents to produce a new offspring swarm by using two novel evolutionary operators, namely, damping-based mutation and inversion-scrambling-based crossover. OSAPSO is given a powerful exploration capacity by adopting the survivor probabilistic selection of SA, which accepts subpar solutions with a certain probability. Finally, PSO itself kicks in, making a good trade-off between solution diversity and convergence speed of the proposed method. Due to the non-convex discontinuous nature of TSCC, OSAPSO is modified to clone it into a discrete optimization problem. Within a heterogeneous cloud computing environment, OSAPSO and eight well-regarded competitors are examined on a set of multi-scale IIoT heterogeneous task groups (realistic and synthetic). In terms of power consumption, monetary cost, service makespan, and system throughput, experimental results reveal that OSAPSO has a winning performance and is statistically more This paragraph of the first footnote will contain the date on which you submitted your paper for review. It will also contain support information, including sponsor and financial support acknowledgment.

show abstract

Section: E Opposition-based Learningmentioning

confidence: 99%