A comprehensive survey on the P system optimization algorithms’ variants and their applications

IF 8.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shipin Yang, Songlu Wang, Wenhua Jiao, Xue Mei, Qing Zhang, Yinqiang Zhang, Lijuan Li
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引用次数: 0

Abstract

Computation inspired by natural phenomena, known as bio-inspired algorithms, is one of the main research directions in natural computing. P system optimization algorithms (POAs), sometimes also called the membrane algorithm, are a branch of bio-inspired algorithms. In light of the fact that they have rigorous and sound theoretical development, as well as providing a parallel distributed framework, POAs have become an emerging class of distributed computing models inspired by the structure and function of biological cells. With P systems developing steadily and more of their variant algorithms being published, new membrane structures and intra-membrane rules continue to appear, boosting the flexibility of P systems. In this paper, we conduct a systematic review of POAs to clarify their development context, application scenarios, and future directions, with the specific work arranged as follows. Firstly, the concepts of the membrane computing model are introduced; secondly, the algorithmic structure and algorithmic procedure of POAs are generalized, followed by a summary and classification of the different POAs’ variants in the light of current literature works. Then, the application areas of POAs are categorized and summed up. Finally, the current issues of POAs and potential future directions of their development are discussed.
P系统优化算法的变体及其应用综述
受自然现象启发的计算被称为生物启发算法,是自然计算的主要研究方向之一。P系统优化算法(POAs),有时也称为膜算法,是仿生算法的一个分支。由于poa具有严格而健全的理论发展,并提供了并行的分布式框架,因此受到生物细胞的结构和功能的启发,poa已经成为新兴的分布式计算模型。随着P系统的稳步发展和越来越多的变体算法的发表,新的膜结构和膜内规则不断出现,增强了P系统的灵活性。本文对poa进行了系统梳理,明确了poa的发展脉络、应用场景和未来发展方向,具体工作安排如下:首先,介绍了膜计算模型的概念;其次,概括了POAs的算法结构和算法流程,并结合当前文献对不同POAs的变体进行了总结和分类。然后,对poa的应用领域进行了分类和总结。最后,讨论了行动纲领目前存在的问题和未来可能的发展方向。
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来源期刊
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
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