Path planning of patrol robot based on improved discrete electrostatic discharge algorithm

Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots.

Multiclass Interactive Martial Arts Teaching Optimization Method Based on Euclidean Distance

Aiming at the problems of low optimization accuracy, poor optimization effect, and long running time in current teaching optimization algorithms, a multiclass interactive martial arts teaching optimization method based on the Euclidean distance is proposed. Using the K-means algorithm, the initial population is divided into several subgroups based on the Euclidean distance, so as to effectively use the information of the population neighborhood and strengthen the local search ability of the algorithm. Imitating the school's selection of excellent teachers to guide students with poor performance, after the “teaching” stage, the worst individual in each subgroup will learn from the best individual in the population, and the information interaction in the evolutionary process will be enhanced, so that the poor individuals will quickly move closer to the best individuals. According to different learning levels and situations of students, different teaching stages and contents are divided, mainly by grade, supplemented by different types of learning groups in the form of random matching, so as to improve the learning ability of members with weak learning ability in each group, which effectively guarantees the diversity of the population and realizes multiclass interactive martial arts teaching optimization. Experimental results show that the optimization effect of the proposed method is better, which can effectively improve the accuracy of algorithm optimization and shorten the running time of the algorithm.

RDF Query Path Optimization Using Hybrid Genetic Algorithms

Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

A Tent Marine Predators Algorithm with Estimation Distribution Algorithm and Gaussian Random Walk for Continuous Optimization Problems

The marine predators algorithm (MPA) is a novel population-based optimization method that has been widely used in real-world optimization applications. However, MPA can easily fall into a local optimum because of the lack of population diversity in the late stage of optimization. To overcome this shortcoming, this paper proposes an MPA variant with a hybrid estimation distribution algorithm (EDA) and a Gaussian random walk strategy, namely, HEGMPA. The initial population is constructed using cubic mapping to enhance the diversity of individuals in the population. Then, EDA is adapted into MPA to modify the evolutionary direction using the population distribution information, thus improving the convergence performance of the algorithm. In addition, a Gaussian random walk strategy with medium solution is used to help the algorithm get rid of stagnation. The proposed algorithm is verified by simulation using the CEC2014 test suite. Simulation results show that the performance of HEGMPA is more competitive than other comparative algorithms, with significant improvements in terms of convergence accuracy and convergence speed.

Behavior model of an intelligent agent based on a genetic algorithm

A solution of improving the behavior model of a non-player character as an intelligent agent by optimizing input parameters based on a genetic algorithm is presented. The proposed approach includes the development of a non-player character model: a skeleton, rigid bodies, the implementation of a dynamic model based on the Featherstone algorithm, and modeling of the character's behavior based on a genetic algorithm. The formation of a behavior model using a genetic algorithm that simulates the physical properties of a character, taking into account his actions, is proposed. The stages of the genetic algorithm include creating an initial population, fitness score, selection, crossing and mutation. Based on the results of the experiments, the input parameters of the non-player character behavior model were determined, maximizing the cumulative fitness score, which acts as an estimate of the reward, which can be used as initial values for further experiments. Keywords: non-player character, intelligent agent, simulation, genetic algorithm

A Modified Jellyfish Search Optimizer with Opposition Based Learning and Biased Passive Swarm Motion

Jellyfish Search Optimizer (JSO) is one of the latest nature inspired optimization algorithms. This paper aims to improve the convergence speed of the algorithm. For the purpose, it identifies two modifications to form a proposed variant. First, it proposes improvement of initial population using Opposition based Learning (OBL). Then it introduces a probability-based replacement of passive swarm motion into moves biased towards the global best. OBL enables the algorithm to start with an improved set of population. Biased moves towards global best improve the exploitation capability of the algorithm. The proposed variant has been tested over 30 benchmark functions and the real world problem of 10-bar truss structure design optimization. The proposed variant has also been compared with other algorithms from the literature for the 10-bar truss structure design. The results show that the proposed variant provides fast convergence for benchmark functions and accuracy better than many algorithms for truss structure design.

A Pilot Study of Natural Formulation Activity in the Protection of Stored Wheat and Barley Against the Stored-Product Insects

The stored-product insects are one of the major causes of losses in the stored cereals. Most of control measures still rely on a synthetic pesticide usage, but due to its negative side effects on the goods, human health, and the environment, there is an urgent need for an alternative control. A natural formulation based on the diatomaceous earth (DE) SilicoSec®, enhanced with the botanicals (essential oil lavender, corn oil, and bay leaves dust) and the silica gel was developed. The aim of the study was to test the activity of the developed formulation as a postharvest protectant of seed wheat and barley in the suppression Sitophilus oryzae (L.), Rhyzopertha dominica (F.) and Tribolium castaneum (Herbst). As a reference comparative value, the DE SilicoSec® was applied. Subsequent to the six months of storage under the simulated warehouse conditions, the formulation has completely suppressed the initial population development of all three tested insect species, both in wheat and barley. In wheat, a complete suppression was detected at the dose of 500 ppm against T. castaneum and 600 ppm against both R. dominica and S. oryzae. In barley, a complete suppression was detected at the doses of 500 ppm, 400 ppm, and 600 ppm against R. dominica, T. castaneum and S. oryzae, respectively. Conclusively, the results of this study indicate that the developed natural formulation based on the DE, botanicals, and silica gel was highly effective against the three major stored‐product insect species, providing a long-term safe storage of wheat and barley seeds.

A VMD–CISSA–LSSVM Based Electricity Load Forecasting Model

Accurate power load forecasting has an important impact on power systems. In order to improve the load forecasting accuracy, a new load forecasting model, VMD–CISSA–LSSVM, is proposed. The model combines the variational modal decomposition (VMD) data preprocessing method, the sparrow search algorithm (SSA) and the least squares support vector machine (LSSVM) model. A multi-strategy improved chaotic sparrow search algorithm (CISSA) is proposed to address the shortcomings of the SSA algorithm, which is prone to local optima and a slow convergence. The initial population is generated using an improved tent chaotic mapping to enhance the quality of the initial individuals and population diversity. Second, a random following strategy is used to optimize the position update process of the followers in the sparrow search algorithm, balancing the local exploitation performance and global search capability of the algorithm. Finally, the Levy flight strategy is used to expand the search range and local search capability. The results of the benchmark test function show that the CISSA algorithm has a better search accuracy and convergence performance. The volatility of the original load sequence is reduced by using VMD. The optimal parameters of the LSSVM are optimized by the CISSA. The simulation test results demonstrate that the VMD–CISSA–LSSVM model has the highest prediction accuracy and stabler prediction results.

Greedy Genetic Algorithm for the Data Aggregator Positioning Problem in Smart Grids

In this work, we present a metaheuristic based on the genetic and greedy algorithms to solve an application of the set covering problem (SCP), the data aggregator positioning in smart grids. The GGH (Greedy Genetic Hybrid) is structured as a genetic algorithm, but it has many modifications compared to the classic version. At the mutation step, only columns included in the solution can suffer mutation and be removed. At the recombination step, only columns from the parent’s solutions are available to generate the offspring. Moreover, the greedy algorithm generates the initial population, reconstructs solutions after mutation, and generates new solutions from the recombination step. Computational results using OR-Library problems showed that the GGH reached optimal solutions for 40 instances in a total of 75 and, in the other instances, obtained good and promising values, presenting a medium gap of 1,761%.

Transient population dynamics drive the spread of invasive wild pigs in North America

Invasion of nonindigenous species is considered one of the most urgent problems affecting native ecosystems and agricultural systems. Mechanistic models that account for short-term population dynamics can improve prediction because they incorporate differing demographic processes that link the environmental conditions of a spatial location explicitly with the invasion process. Yet short-term population dynamics are rarely accounted for in spatial models of invasive species spread. Accounting for transient population dynamics, we predict the population growth rate and establishment probability of wild pigs following introduction into any location in North America. We compared predicted population growth rate with observed geographic rates of spread and found significant relationships between the annual rate of spread and population growth rates. We used geospatial data on the distribution of mast producing tree species (a principle forage resource of wild pigs) and agricultural crops that can replace mast in their diets to predict population dynamics using transient population simulations. We simulated populations under different initial population sizes (i.e. number of introduced individuals, often termed propagule size) and for different amounts of time following introduction. By varying the initial population size and simulation time, we were able to identify areas in North America with high probability for establishment of wild pigs if introduced. Our findings can be used to inform surveillance and removal efforts to reduce the potential for establishment and spread of wild pigs.