Improved Biogeography-Based Optimization Algorithm by Hierarchical Tissue-Like P System with Triggering Ablation Rules

Abstract

BBO is one of the new metaheuristic optimization algorithms, which is based on the science of biogeography. It can be used to solve optimization problems through the migration and drift of species between habitats. Many improved BBO algorithms have been proposed, but there were still many shortcomings in global optimization, convergence speed, and algorithm complexity. In response to the above problems, this paper proposes an improved BBO algorithm (DCGBBO) by hierarchical tissue-like P system with triggering ablation rules. Membrane computing is a branch of natural computing that aims to abstract computational models (P system) from the structure and function of biological cells and from the collaboration of cell groups such as organs and tissues. In this paper, firstly, a dynamic crossover migration operator is generated to improve the global search ability and also increase the species diversity. Secondly, a dynamic Gaussian mutation operator is introduced to speed up convergence and improve local search capabilities. To guarantee the correctness and feasibility of the mutation, a unified maximum mutation rate is designed. Finally, a hierarchical tissue-like P system with triggering ablation rules is combined with the DCGBBO algorithm, making use of evolution rules and communication rules to achieve migration and mutation of solutions and reduce computational complexity. In the experiments, eight classic benchmark functions and CEC 2017 benchmark functions are applied to demonstrate the effect of our algorithm. We apply the proposed algorithm to segment four colour pictures, and the results proved to be better compared to other algorithms.

An Improved Biogeography-based Optimization with Hybrid Migration and Feedback Differential Evolution and its Performance Analysis

“…Recently, Sang et al (2021) proposed an improved BBO algorithm by hierarchical tissue-like P system with triggering ablation rules in view of many shortcomings of BBO in terms of global optimization, convergence speed and algorithm complexity, which is named DCGBBO. Sang et al first proposed a dynamic crossover migration operator to improve the global search capability and increase species diversity.…”

“…If the traditional differential evolution algorithm is static, the purpose of the improvement is to make the algorithm dynamic and flexible, and can dynamically choose the mutation mode according to the s earch environment. EM BBO (Zhang et al 2019a), TDBBO (Zhao et al 2019) and DCGBBO (Sang et al 2021) all add differential mutation operator to improve the search ability of original BBO. However, these algorithms only design dynamic transformation modes with two mutation modes at most.…”

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

“…SNSbased MIEAs are presented by using tissue-like P systems or neural-like P systems with various network topologies [68]. Various meta-heuristic algorithms, such as GA [69], DE [70] and its variants [71,72], PSO [73,74], ABC [75], and BBO [76], are usually introduced to SNS-based MIEAs as the basic evolutionary operation in the cell or neural [77][78][79][80][81]. The membrane structure in DNS-based MIEAs can be dynamically changed according to communication channel rules, and this class of MIEAs, with an extended membrane structure, has great potential for solving complex problems [82,83].…”

An improved multi-view attention network inspired by coupled P system for node classification

“…Membrane system models are widely used in engineering optimization, power system fault diagnosis, ecosystem modeling and other aspects [ 11 , 12 ]. In terms of application, many extended membrane algorithms have been proposed to solve problems and have been optimized in improving the efficiency of the algorithm and reducing the time complexity [ 13 – 15 ]. Based on tissue-like P system with promoters and inhibitors, an algorithm called ECTPPI-Apriori [ 16 ] is also proposed.…”