Sensor ontology is able to resolve the sensor data heterogeneity issue among the Cybertwin-driven 6G based Internet of Everything (IoE) systems. However, due to human subjectivity, the sensor ontologies also suffer from the heterogeneity problem. To address this problem, it is necessary to execute the sensor Ontology Matching (OM) process, i.e., finding the identical entity pairs between two ontologies. To this end, we first propose a Multi-Context based ESM (MC-ESM) to measure the similarity of two entities by taking into consideration their semantic contexts. After that, a parallel compact Differential Evolution with Adaptive Step Length (pcDE-ASL) is proposed to find all entity mappings, which uses the probability presentation on the population to save the memory consumption, and the parallel processing mechanism and ASL to help the algorithm efficiently converge on the global optima. The experimental results show that pcDE-ASL is both effective and efficient.

Matching Sensor Ontologies With Multi-Context Similarity Measure and Parallel Compact Differential Evolution Algorithm

Artificial bee colony algorithm with an adaptive search manner and dimension perturbation

The PID (proportional–integral–derivative) controller is the most widely used control method in modern engineering control because it has the characteristics of a simple algorithm structure and easy implementation. The traditional PID controller, in the face of complex control objects, has been unable to meet the expected requirements. The emergence of the intelligent algorithm makes intelligent control widely usable. The Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a new evolutionary algorithm. Compared with other intelligent algorithms, the QUATRE algorithm has a strong global search ability. To improve the accuracy of the algorithm, the adaptive mechanism of online adjusting control parameters was introduced and the linear population reduction strategy was adopted to improve the performance of the algorithm. The standard QUATRE algorithm, particle swarm optimization algorithm and improved QUATRE algorithm were tested by the test function. The experimental results verify the advantages of the improved QUATRE algorithm. The improved QUATRE algorithm was combined with PID parameters, and the simulation results were compared with the PID parameter tuning method based on the particle swarm optimization algorithm and standard QUATRE algorithm. From the experimental results, the control effect of the improved QUATRE algorithm is more effective.

A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm

In the original artificial bee colony (ABC), only one dimension of the solution is updated each time and this leads to little differences between the offspring and the parent solution. Then, it affects the convergence speed. In order to accelerate the convergence speed, we can update multiple dimensions of the solution at the same time, and the information of the global optimal solution can be used for guidance. However, using these two methods will reduce the population diversity at the initial stage. This is not conducive to search of multimodal functions. In this paper, a population diversity guided dimension perturbation for artificial bee colony algorithm (called PDDPABC) is proposed, in which population diversity is used to control the number of dimension perturbations. Then, it can maintain a certain population diversity, and does not affect the convergence speed. In order to verify the performance of PDDPABC, we tested its performance on 22 classic problems and CEC 2013 benchmark set. Compared with several other ABC variants, our approach can achieve better results.

Population Diversity Guided Dimension Perturbation for Artificial Bee Colony Algorithm

The Phasmatodea Population Evolution (PPE) algorithm, inspired by the evolution of the phasmatodea population, is a recently proposed meta-heuristic algorithm that has been applied to solve problems in engineering. Chaos theory has been increasingly applied to enhance the performance and convergence of meta-heuristic algorithms. In this paper, we introduce chaotic mapping into the PPE algorithm to propose a new algorithm, the Chaotic-based Phasmatodea Population Evolution (CPPE) algorithm. The chaotic map replaces the initialization population of the original PPE algorithm to enhance performance and convergence. We evaluate the effectiveness of the CPPE algorithm by testing it on 28 benchmark functions, using 12 different chaotic maps. The results demonstrate that CPPE outperforms PPE in terms of both performance and convergence speed. In the performance analysis, we found that the CPPE algorithm with the Tent map showed improvements of 8.9647%, 10.4633%, and 14.6716%, respectively, in the Final, Mean, and Standard metrics, compared to the original PPE algorithm. In terms of convergence, the CPPE algorithm with the Singer map showed an improvement of 65.1776% in the average change rate of fitness value, compared to the original PPE algorithm. Finally, we applied our CPPE to stock prediction. The results showed that the predicted curve was relatively consistent with the real curve.

/pdf/cppe-an-improved-phasmatodea-population-evolution-algorithm-1ninzfpm.pdf

CPPE: An Improved Phasmatodea Population Evolution Algorithm with Chaotic Maps

Multi-group Flower Pollination Algorithm (MFPA) based on novel communication strategies was proposed with an eye to the disadvantages of the Flower Pollination Algorithm (FPA), such as tardy convergence rate, inferior search accuracy, and strong local optimum. By introducing a parallel operation to divide the population into some groups, the global search capability of the algorithm was improved. Then three new communication strategies were proposed. Strategy 1 combined high-quality pollens of each group for evolution and replaced the old pollens. Strategy 2 let each group’s inferior pollens approaching to the optimal pollen. Strategy 3 was a combination of strategies 1 and 2. Then, experiments on 25 classical test functions show that MFPA based on novel communication strategies has a good global optimization ability, improving the convergence speed and accuracy of the FPA. Thus, we compare MFPA using three strategies with FPA and PSO, its result shows that MFPA is better than FPA and PSO. Finally, we also applied it to two practical problems and achieved a better convergence effect than FPA.

/pdf/multi-group-flower-pollination-algorithm-based-on-novel-1yk0y5zsks.pdf

Multi-group Flower Pollination Algorithm Based on Novel Communication Strategies

Capacitated Vehicle Routing Problem (CVRP) is difficult to solve by the traditional precise methods in the transportation area. The metaheuristic algorithm is often used to solve CVRP and can obtain approximate optimal solutions. Phasmatodea population evolution algorithm (PPE) is a recently proposed metaheuristic algorithm. Given the shortcomings of PPE, such as its low convergence precision, its nature to fall into local optima easily, and it being time-consuming, we propose an advanced Phasmatodea population evolution algorithm (APPE). In APPE, we delete competition, delete conditional acceptance and correspondingevolutionary trend update, and add jump mechanism, history-based searching, and population closing moving. Deleting competition and conditional acceptance and correspondingevolutionary trend update can shorten PPE running time. Adding a jump mechanism makes PPE more likely to jump out of the local optimum. Adding history-based searching and population closing moving improves PPE’s convergence accuracy. Then, we test APPE by CEC2013. We compare the proposed APPE with differential evolution (DE), sparrow search algorithm (SSA), Harris Hawk optimization (HHO), and PPE. Experiment results show that APPE has higher convergence accuracy and shorter running time. Finally, APPE also is applied to solve CVRP. From the test results of the instances, APPE is more suitable to solve CVRP.

/pdf/advanced-phasmatodea-population-evolution-algorithm-for-2y8vesu3.pdf

Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem

Advanced equilibrium optimizer for electric vehicle routing problem with time windows

BACKGROUND
Beckmannia syzigachne (Steud.) Fernald has evolved herbicide resistance due to the long-term sole use of herbicides and has become a dominant weed in wheat fields in the middle and lower reaches of the Yangtze River in China. In addition to the selection pressure imposed by herbicides, pollen-mediated gene flow (PMGF) has been reported to cause the spread of herbicide resistance between populations within a certain range in some farmland weeds. It is not clear whether the same is true for the self-pollinated grass weed B. syzigachne.


RESULTS
In this study, we confirmed and quantified the level of PMGF in B. syzigachne through concentric circle planting and herbicide resistance tests. Results show that when the B. syzigachne pollen donor was close to the recipient (0.5 m), the average gene flow was 0.66%. Gene flow was detected as far as 10 m (the farthest distance studied) and decreased exponentially with increasing distance, which could be described by a double exponential decay model. Temperature also affected gene flow, whilst the average level of gene flow in all directions of wind was similar and wind speed caused insignificant difference in gene flow.


CONCLUSION
The results of this study confirmed that PMGF can occur between B. syzigachne populations in adjacent fields. Although the level of resistance spreading by pollen was low, especially across long distance, the results were relevant for smallholding farms, which is the dominant form of agricultural operation in China. It is therefore important to take proactive measures and integrate chemical and ecological weed control methods to prevent the spread of resistant B. syzigachne via both seeds and pollens. This article is protected by copyright. All rights reserved.

Jiawen Zhuang

Papers

Multi-group Flower Pollination Algorithm Based on Novel Communication Strategies

Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem

Advanced equilibrium optimizer for electric vehicle routing problem with time windows

Pollen-mediated flow of herbicide resistance genes in Beckmannia syzigachne.