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Wild Horse Optimizer (WHO) is a swarm-based meta-heuristic algorithm inspired by animal behavior, which mainly imitates the decent behavior, grazing behavior, mating behavior and leadership dominance behavior of wild horses in nature in their lives for finding the optimal. The location update method of WHO is prone to low convergence accuracy, poor global search ability and local optimum problems. With the aim of balancing global searchability and exploitation performance, an improved WHO that incorporates a dual weight starvation strategy and a random convergence factor is proposed. In the exploration stage, the starvation strategy is inspired by the starvation characteristics of animals, so that the algorithm can continuously adjust the stallion position according to the starvation level and automatically adjust the distance between the stallion and the wild horse according to the changing adaptation value, which improves the global search performance and can jump out of the local optimum at the same time. In the exploitation stage, a convergence factor is added to help it jump out of the local optimum and continue to search for a better solution. The simulation experiment on 23 benchmark functions is to verify the effectiveness of the proposed algorithm being compared with Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Rat Swarm Optimizer (RSO), Multi-Verse Optimizer (MVO), Gray Wolf Optimizer (GWO) and Artificial Bee Swarm Optimizer (ALO). Finally two real engineering design problems were solved. The simulation results show that the proposed SD3WHO has a strong seeking capability and optimization performance. [ABSTRACT FROM AUTHOR]

The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rapid convergence, they have low accuracy and can easily become trapped in local optima. To address these issues, a new heuristic algorithm was proposed, a gradient descent-based RNA genetic algorithm. Specifically, adaptive moment estimation (Adam) was employed as a mutation operator to improve the local development ability of the algorithm. Additionally, two new operators inspired by the inner-loop structure of RNA molecules were introduced: an inner-loop crossover operator and an inner-loop mutation operator. These operators enhance the global exploration ability of the algorithm in the early stages of evolution and enable it to escape from local optima. The algorithm consists of two stages: a pre-evolutionary stage that employs RNA genetic algorithms to identify individuals in the vicinity of the optimal region and a post-evolutionary stage that applies a adaptive gradient descent mutation to further enhance the solution's quality. When compared with the current advanced algorithms for solving function optimization problems, Adam RNA Genetic Algorithm (RNA-GA) produced better optimal solutions. In comparison with RNA-GA and Genetic Algorithm (GA) across 17 benchmark functions, Adam RNA-GA ranked first with the best result of an average rank of 1.58 according to the Friedman test. In the set of 29 functions of the CEC2017 suite, compared with heuristic algorithms such as African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Optimization Algorithm, and Grey Wolf Optimizer, Adam RNA-GA ranked first with the best result of an average rank of 1.724 according to the Friedman test. Our algorithm not only achieved significant improvements over RNA-GA but also performed excellently among various current advanced algorithms for solving function optimization problems, achieving high precision in function optimization. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of Guangdong-Hong Kong-Macao Greater Bay Area, the passenger flow between cities in the region has increased rapidly, and the requirements for transportation efficiency of intercity railways are getting higher and higher. Moving block and automatic turn-back technology is an important development direction of signal system to improve railway transportation efficiency, which can effectively shorten the tracking interval between trains and the turn-back interval of turn-back stations, thus improving the transportation capacity and efficiency of lines. At present, moving block technology and automatic turn-back have not been applied in intercity railway. According to the technical status of moving block and automatic turn-back, the feasibility of function optimization of existing intercity CTCS2+ATO train control system in Pearl River Delta is deeply analyzed in this paper, and the overall framework of realizing moving block and automatic turn-back in the intercity CTCS2+ATO train control system in Pearl River Delta is put forward. Finally, the working principle and realization of CCS block mode switching, driving permit calculation, automatic turn-back function optimization and turn-back process are introduced emphatically. The results show that it is feasible to realize moving block and automatic turn-back under the condition that the existing intercity CTCS2+ATO train control system in the Pearl River Delta is slightly changed, which is of great significance to improve the transportation efficiency of intercity railway lines in the Pearl River Delta. [ABSTRACT FROM AUTHOR]

Abstract As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence speed, solution accuracy, and local minima escaping ability of the traditional GWO algorithm, this work proposes a multi-strategy fusion improved gray wolf optimization (IGWO) algorithm. First, the initial population is optimized using the lens imaging reverse learning algorithm for laying the foundation for global search. Second, a nonlinear control parameter convergence strategy based on cosine variation is proposed to coordinate the global exploration and local exploitation ability of the algorithm. Finally, inspired by the tunicate swarm algorithm (TSA) and the particle swarm algorithm (PSO), a nonlinear tuning strategy for the parameters, and a correction based on the individual historical optimal positions and the global optimal positions are added in the position update equations to speed up the convergence of the algorithm. The proposed algorithm is assessed using 23 benchmark test problems, 15 CEC2014 test problems, and 2 well-known constraint engineering problems. The results show that the proposed IGWO has a balanced E&P capability in coping with global optimization as analyzed by the Wilcoxon rank sum and Friedman tests, and has a clear advantage over other state-of-the-art algorithms.

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms. [ABSTRACT FROM AUTHOR]

The Honey Badger Algorithm (HBA) is a new swarm intelligence optimization algorithm by simulating the foraging behavior of honey badgers in nature. To further improve its convergence speed and convergence accuracy, an improved HBA based on the density factors with the elementary functions and the mathematical spirals in the polar coordinate system was proposed. The algorithm proposes six density factors for attenuation states based on elementary functions, and introduces mathematical expressions of the polar diameters and angles of seven mathematical spirals (Fibonacci spiral, Butterfly curve, Rose spiral, Cycloid, Archimedean spiral, Hypotrochoid and Cardioid) in the polar coordinate system based on the density factors with the best synthesized effect to replace the foraging strategy of honey badger digging pattern in HBA. By using 23 benchmark test functions, the above improvements are sequentially compared with the original HBA, and the optimization algorithm with the best improvement, α4CycρHBA, is selected to be compared with SOA, MVO, DOA, CDO, MFO, SCA, BA, GWO and FFA. Finally, four engineering design problems (pressure vessel design, three-bar truss design, cantilever beam design and slotted bulkhead design) were solved. The simulation experiments results show that the proposed improved HBA based on the density factors with the elementary functions and the mathematical spirals of the polar coordinate system has the characteristics of balanced exploration and expiration, fast convergence and high accuracy, and is able to solve the function optimization and engineering optimization problems in a better way. [ABSTRACT FROM AUTHOR]

A new swarm intelligence optimization algorithm is proposed to simulate the long-distance flight process of dandelion seeds as a function of wind by the dandelion optimizer (DO). An improved version of DO, called IDO, is proposed to overcome the shortcomings of slow optimization speed and vulnerability to local optimum of the algorithm. In the landing stage of the dandelion seeds, IDO introduces the elite pool roulette wheel selection mechanism to maintain the diversity of the dandelion population, which enables the algorithm to maintain a better optimization ability. In the rising stage, a gamma distribution is introduced to make the algorithm population more diverse, thus improving its optimization capabilities. Finally, the mirror reversal strategy is introduced to search for the mirror solutions of inferior particles in the population surrounding the optimal individual, which helps the population to escape from local optima. The performance of the proposed improved IDO algorithm was tested against the CEC-2017, CEC-2020, and CEC-2022 benchmark functions. Finally, three engineering design problems were optimized, namely, three-bar truss design, cantilever design, and pressure vessel design. The experimental results show that the proposed IDO algorithm significantly improves the convergence rate and optimization accuracy. The experimental results show that the proposed IDO algorithm optimizes the exploitation and exploration process, improves the convergence rate and optimization accuracy. [ABSTRACT FROM AUTHOR]

In the pursuit of enhancing harmonic detection precision within microgrids, this paper introduces a pioneering algorithm, VMD-DCHHO-HD, which amalgamates Variational Mode Decomposition (VMD) with an advanced Harris Hawk Optimization algorithm characterized by dynamic opposition-based learning and Cauchy mutation (DCHHO). This study establishes a fitness function based on Shannon entropy, thereby minimizing the Local Minimum Entropy (LME) as the optimization objective for DCHHO. Building upon this, the VMD crucial parameters are efficiently identified using the enhanced HHO algorithm (DCHHO), enabling precise decomposition of complex voltage signals. The proposed method effectively addresses issues commonly encountered in traditional Empirical Mode Decomposition (EMD) during harmonic analysis, such as mode mixing, endpoint effects, and significant errors. Notably, it adeptly captures harmonic components spanning diverse frequencies, offering a nuanced solution to common pitfalls in traditional methodologies. In simulation experiments, VMD-DCHHO-HD showcases remarkable proficiency in extracting microgrid voltage signals, excelling at discerning high-order, low-amplitude harmonic components amid noise. The algorithm’s superior precision and heightened reliability, as affirmed by comparative analyses against existing methods, position it as an advanced tool for precise and robust harmonic analysis in microgrid systems.

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In recent years, China's railroads have been rapidly developed, but to improve the quality of service to customers and the rapid passage of passengers is a major problem facing the railroad operation. It is the era of information development at present, China's railroad operation has also been unprecedented rapidly developed and has significant achievements. In order to meet the growing demand and improve the level of information technology, the information system platform for railroad freight transportation has been created, because each system is not connected with each other and is independent of each other, thus creating an upgrade of the system. To solve the above-mentioned problems, it is necessary first of all to establish an intelligent freight station inquiry system, which is one of the ways for the customers to experience the convenience directly. In this paper, by understanding and analyzing the inquiry system of freight stations, we address the shortcomings and deficiencies of the system now. Using the development concept of information technology and the general development goals of Chinese railroad passenger and freight stations, the development has been carried out through the two levels of the total structure and performance of the system. Current successful modifications and enhancements of the Chinese train transportation system show promising experimental results. It has been confirmed by these tests that the system demonstrates a high level of stability and dependability while skillfully meeting the intelligent inquiry needs of travelers at railroad freight stations. [ABSTRACT FROM AUTHOR]

This experimental research examined the effectiveness of using chatbots in English as a Foreign Language (EFL) classrooms at a Chinese elementary school. Seventy-four students were divided into two groups: one employing traditional methods, and the other using chatbots. Before and after the 3-month teaching period, pre- and post-tests were used to measure students’ oral English proficiency, and a questionnaire was employed to assess their willingness to communicate (WTC). Data from interviews with teachers and students provided insights into chatbot usage and modification. Data analysis involved using SPSS to identify significant differences in pre- and post-test scores and NVivo 11 to code qualitative feedback from student and teacher interviews. The results show that chatbot integration significantly improved oral English proficiency and WTC in the experimental group compared with the control group. Teachers can enhance instruction by adopting tailored chatbot features to refine teaching methods, thus creating a more adaptive learning path for students. Further, chatbot improvements such as personalised error correction and user-centric features pave the way for immersive and fruitful language learning. This study provides valuable insights for integrating chatbots into language classrooms and suggests opportunities for further chatbot enhancements.. [ABSTRACT FROM AUTHOR]

Wild Horse Optimizer (WHO) is a population-based metaheuristic algorithm inspired by animal behavior, which mainly imitates the decent behavior, grazing behavior, mating behavior and leadership dominance behavior of wild horses in nature to find the optimal. The initialization of the population by imitating the behavior of wild horses is prone to uneven distribution of population positions, and its position updating method is prone to local optimal problems while improving the efficiency of the search. In order to enhance the population diversity and to break out of the local optimum, an adaptive weighted wild horse optimizer based on backward learning and small-hole imaging strategy is proposed. The backward learning strategy is used to enhance the population diversity and improve the uneven distribution of individuals; The adaptive weight and small-hole imaging strategy are added to the local search strategy to improve the global search ability and jump out of the local optimum. To verify the effectiveness of the proposed algorithm, simulation experiments were conducted by using 23 benchmark test functions to test the search ability and Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Rat Swarm Optimizer (RSO) and Multi-Verse Optimizer (MVO) algorithms are compared in terms of their search performance, and finally four real engineering design problems are solved. The simulation results indicate that the proposed FHPWHO has excellent merit-seeking capability. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.

To explore whether dance movement techniques in colleges and universities in the context of artificial intelligence can bring new developments in teaching methods in the field of teaching and whether its effectiveness can bring some new power to professional training. In this paper, the principle model of the dance technique movement system is used to calculate the correlation coefficient between music and dance movement characteristics. On this basis combined with a genetic algorithm to train the corresponding relationship of different music dance movements, through the accuracy of the corresponding relationship as an adaptation function, to obtain an optimal dance movement correspondence, to get the dance movement matching optimization objective function, and combined with ant colony theory to optimize the solution of this objective function, thus completing the optimization of dance movement matching in music choreography. The research results show that 83.5% of the students think that dance movement skills are relevant in teaching and can achieve the teaching effect faster and more effectively, and 70% of the students think that they have enough learning time to grasp the form of technical movements more skillfully. Therefore, it is very important to analyze the dance movement techniques in colleges and universities to teach and improve the quality of teaching.

Symmetry in a differential evolution (DE) transforms a solution without impacting the family of solutions. For symmetrical problems in differential equations, DE is a strong evolutionary algorithm that provides a powerful solution to resolve global optimization problems. DE/best/1 and DE/rand/1 are the two most commonly used mutation strategies in DE. The former provides better exploitation while the latter ensures better exploration. DE/Neighbor/1 is an improved form of DE/rand/1 to maintain a balance between exploration and exploitation which was used with a random neighbor-based differential evolution (RNDE) algorithm. However, this mutation strategy slows down convergence. It should achieve a global minimum by using 1000 × D, where D is the dimension, but due to exploration and exploitation balancing trade-offs, it can not achieve a global minimum within the range of 1000 × D in some of the objective functions. To overcome this issue, a new and enhanced mutation strategy and algorithm have been introduced in this paper, called DE/Neighbor/2, as well as an improved random neighbor-based differential evolution algorithm. The new DE/Neighbor/2 mutation strategy also uses neighbor information such as DE/Neighbor/1; however, in addition, we add weighted differences after various tests. The DE/Neighbor/2 and IRNDE algorithm has also been tested on the same 27 commonly used benchmark functions on which the DE/Neighbor/1 mutation strategy and RNDE were tested. Experimental results demonstrate that the DE/Neighbor/2 mutation strategy and IRNDE algorithm show overall better and faster convergence than the DE/Neighbor/1 mutation strategy and RNDE algorithm. The parametric significance test shows that there is a significance difference in the performance of RNDE and IRNDE algorithms at the 0.05 level of significance. [ABSTRACT FROM AUTHOR]

Teaching–Learning-Based Optimization (TLBO) is a population-based intelligent optimization algorithm, which simulates the "teaching" process of teachers to students and the "learning" process of students in the class. In order to solve the problems of slow optimization speed, low optimization accuracy and easy to fall into local optimization, an improved TLBO algorithm based on Cauchy mutation and chaos operators are proposed. Firstly, the dynamic selection of teachers in the "teaching" stage leads to higher class average grades. Learning from the best students in the class during the "learning" phase makes class results more focused. Secondly, after a teaching is completed, Cauchy mutation is carried out to make the algorithm population more diverse so as to get rid of the local optimal solution. Finally, on the basis of Cauchy mutation, chaos theory is introduced into the optimization process of TLBO algorithm, and 10 chaos are embedded in the process of generating random numbers by Cauchy mutation, which enhances its ergo city and irreconcilability to further improve its convergence speed and accuracy. The performance of the proposed improved TLBO algorithm was tested by using 30 benchmark functions in CEC-BC-2017, and finally two engineering design problems (cantilever arm design and pressure vessel design) were optimized. The experimental results show that the proposed TLBO algorithm has significantly improved its convergence speed and optimization accuracy. [ABSTRACT FROM AUTHOR]

The reptile search algorithm (RSA) is a dynamic and effective meta-heuristic algorithm inspired by the behavior of crocodiles in nature and the way of hunting prey. Unlike other crawler search algorithms, it uses four novel mechanisms to update the location of the solutions, such as walking at high or on the belly, and hunting in a coordinated or cooperative manner. In this algorithm, the total number of iterations is divided into four intervals, and different position-updating strategies are used to make the algorithm easily fall into the local optimum. Therefore, an improved reptile search algorithm based on a mathematical optimization accelerator (MOA) and elementary functions is proposed to improve its search efficiency and make it not easily fall into local optimum. MOA was used to realize the switching of RSA's four searching modes by introducing random perturbations of six elementary functions (sine function, cosine function, tangent function, arccosine function, hyperbolic secant function and hyperbolic cosecant function), four mechanisms are distinguished by random number instead of the original RSA algorithm's inherent four mechanisms by iteration number, which increases the randomness of the algorithm and avoids falling into local optimum. The random perturbations generated by elementary functions are added to the variation trend of parameter MOA to improve the optimization accuracy of the algorithm. To verify the effectiveness of the proposed algorithm, 30 benchmark functions in CEC2017 were used for carrying out simulation experiments, and the optimization performance was compared with BAT, PSO, ChOA, MRA and SSA. Finally, two practical engineering design problems are optimized. Simulation results show that the proposed sechRSA has strong global optimization ability. [ABSTRACT FROM AUTHOR]

Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican's natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. [ABSTRACT FROM AUTHOR]

This paper proposes an arithmetic optimization algorithm that integrates the sine-cosine strategy to address the problems of low solution accuracy, slow convergence speed, and easy fall into local optima in arithmetic optimization algorithms. The algorithm adaptively adjusts the math optimizer acceleration (MOA) accelerator function based on the change information of individual fitness, balancing the global exploration and local exploitation abilities of the algorithm. The improved sine-cosine algorithm is introduced into the local development stage of the algorithm, increasing the population diversity in the later iterations, avoiding the algorithm from falling into local optima, and effectively improving the solution accuracy and convergence speed of the algorithm. Simulation experiments on 14 benchmark test functions show that the improved algorithm has significant improvements in solution accuracy, convergence speed, and robustness. Finally, the improved algorithm is applied to the optimization of support vector machine (SVM) parameters, and a student knowledge level prediction model is established, which further verifies the practicality and superiority of the algorithm. [ABSTRACT FROM AUTHOR]

In view of the problems of low convergence accuracy and falling into local optimum when solving complex problems, a sparrow search algorithm based on adaptive t-distribution and random walk is proposed in this study. In the initialization process, the algorithm uses reverse learning to generate reverse solutions from which excellent individuals are selected to form the initial population. In the original sparrow search algorithm, the adaptive t-distribution strategy and Gaussian random walk strategy are used to improve the optimization ability of the sparrow individuals, and can prevent the algorithm from premature. The simulation results show that the proposed algorithm improves the convergence accuracy and convergence speed when compared with the comparison algorithm. [ABSTRACT FROM AUTHOR]

Future search algorithm imitates the person living life. If one person finds that his life is not good, he will try to change his living life, and he will imitate a more successful person. To overcome insufficient performances of the basic Future search algorithm, this paper proposed an improved Future search algorithm based on the sine cosine algorithm (FSASCA). The proposed algorithm uses sine cosine algorithm to loop-progressive find the best solution. The searching method of the sine cosine algorithm can make the feasible solution to be re-positioned around another feasible solutions, which can make the proposed algorithm have a strong exploitation ability. Four coefficient factors are added in the basic FSA, and new update methods are introduced in the searching phase. To verify the searching and optimization performances of the proposed algorithm in this paper, this paper also gives data calculation results, Wilcoxon rank sum test, iteration figures, box plot figures, and searching path figures. Experimental results showed that FSASCA has a better iteration speed, the convergence precision, the solving accuracy, the strong competitive, and the high balance.

An improved hybrid grey wolf optimization algorithm (IHGWO) is proposed to solve the problem of population diversity, imbalance of exploration and development capabilities, and premature convergence. The algorithm benefits from particle swarm optimization and a dimension learning-based hunting search strategy. In the particle swarm algorithm search strategy, linear variable social learning and self-learning are introduced to improve the population’s ability to communicate information. The individual position, current iteration optimal position, and optimal population position of grey wolves are combined to update the individual position information, thus strengthening the communication between individuals and the population. In the dimension learning-based hunting search strategy, neighborhoods are built for each search member, and neighborhood members can share information, balance global and local searches, and maintain diversity. To validate the algorithm, 23 typical benchmark functions, CEC2022 benchmark functions, and engineering problem sinusoidal low-order-polynomial prediction of positioning error of numerical control machine tools are used to optimize the algorithm’s parameters. Results are compared with those from four other algorithms and analyzed using Friedman’s statistical test. Experimental and statistical tests reveal that the IHGWO algorithm has the best overall benchmark function rating, with an overall effectiveness of 87.23%. In the engineering parameter optimization problem, the mean square error, root mean square error, and goodness of fit of the prediction equation after IHGWO algorithm optimization are 95.3761, 9.7661, and 97.47%, respectively. These numerical values are superior to those of the compared algorithms, effectively demonstrating the comprehensive performance and applicability of the algorithm.

With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. The arithmetic optimization algorithm (AOA) design is inspired by the distribution behavior of the main arithmetic operators in mathematics, including multiplication (M), division (D), subtraction (S) and addition (A). In order to improve the global search ability and local development ability of the AOA, the Lorentz triangle search variable step coefficient was proposed based on the broad-spectrum trigonometric functions combined with the Lorentz chaotic mapping strategy, which include a total of 24 search functions in four categories, such as regular trigonometric functions, inverse trigonometric functions, hyperbolic trigonometric functions, and inverse hyperbolic trigonometric functions. The position update was used to improve the convergence speed and accuracy of the algorithm. Through test experiments on benchmark functions and comparison with other well-known meta-heuristic algorithms, the superiority of the proposed improved AOA was proved. [ABSTRACT FROM AUTHOR]

The numerical solution of dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Therefore, we propose improved seagull optimization algorithm (ISOA) combined with random division method to solve dynamic optimization problems. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull's attack behavior to make the group approach the best position. Secondly, this paper uses the 14 benchmark test functions to verify ISOA. Finally, the improved seagull optimization algorithm is combined with the random division method to solve two chemical dynamic optimization problems. The experimental results show that ISOA algorithm has better performance in function optimization. [ABSTRACT FROM AUTHOR]

Harris hawks optimizer (HHO) is a relatively novel meta-heuristic approach that mimics the behavior of Harris hawk over the process of predating the rabbits. The simplicity and easy implementation of HHO have attracted extensive attention of many researchers. However, owing to its capability to balance between exploration and exploitation is weak, HHO suffers from low precision and premature convergence. To tackle these disadvantages, an improved HHO called VGHHO is proposed by embedding three modifications. Firstly, a novel modified position search equation in exploitation phase is designed by introducing velocity operator and inertia weight to guide the search process. Then, a nonlinear escaping energy parameter E based on cosine function is presented to achieve a good transition from exploration phase to exploitation phase. Thereafter, a refraction-opposition-based learning mechanism is introduced to generate the promising solutions and helps the swarm to flee from the local optimal solution. The performance of VGHHO is evaluated on 18 classic benchmarks, 30 latest benchmark tests from CEC2017, 21 benchmark feature selection problems, fault diagnosis problem of wind turbine and PV model parameter estimation problem, respectively. The simulation results indicate that VHHO has higher solution quality and faster convergence speed than basic HHO and some well-known algorithms in the literature on most of the benchmark and real-world problems. [ABSTRACT FROM AUTHOR]

The basic marine predators algorithm ( MP A) has some drawbacks such as slow convergence speed, low precision, and easy to fall into local optima when solving complex optimization problems. In order to overcome these shortcomings, this paper proposed a planar-mirror reflection imaging learning-based MPA ( PRIL-MPA). In the early stage of the iteration, the PRIL-MPA introduced the strategy that the current global optimal individual guides other individuals in the group to search, so as to accelerate the convergence speed of the algorithm. In the later stage of the iteration, the PRIL-MPA introduced the planar mirror reflection imaging strategy to escape from the local optimization and improve the accuracy of the solution. On the one hand, a comparison of PRIL-MPA with four other algorithms on 12 benchmark test functions shows that the proposed algorithm achieves significant improvement in terms of the solution precision and convergence speed. On the other hand, this paper applied PRIL-MPA to solve 21 feature selection problems, which shows that the proposed algorithm can effectively remove redundant features and improve the accuracy of data classification. Compared with other algorithms, the performance of the proposed algorithm is more competitive. [ABSTRACT FROM AUTHOR]

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Metaheuristic algorithms are effectively used in searching some optical solution space. for optical solution. It is basically the type of local search generalization that can provide useful solutions for issues related to optimization. Several benefits are associated with this type of algorithms due to that such algorithms can be better to solve many issues in an effective way. To provide fast and accurate solutions to huge range of complex issues is one main benefit metaheuristic algorithms. Some metaheuristic algorithms are effectively used to classify the problems and BAT Algorithm (BA) is one of them is more popular in use to sort out issues related to optimization of theoretical and realistic. Sometimes BA fails to find global optima and gets stuck in local optima because of the absence of investigation and manipulation. We have improved the BA to boost its local searching ability and diminish the premature problem. An improved equation of search with more necessary information through the search is set for the generation of the solution. Test set of benchmark functions are utilized to verify the proposed method's performance. The results of simulation showed that proposed methods are best optimal solution as compare to others. [ABSTRACT FROM AUTHOR]

The basic seagull optimization algorithm(SOA) has some drawbacks such as low solution accuracy, slow convergence rate, and easy to fall into local optima when solving complex function optimization problems. This paper proposes an improve SOA based on somersault foraging strategy(abbreviated as SFSOA). First, the proposed SFSOA updates the positions of seagull individuals by using nonlinearly decrease strategy of control parameter A based on the inverted sigmoid function. This way improves individual solution quality and accelerates convergence speed of algorithm. Then, a novel learning mechanism based on the somersault foraging strategy is introduced for the global best individual to increase the position diversity of seagull individual and avoid the algorithm falling into a local optimum at the lately stage. The proposed SFSOA is compared against the basic SOA, grey wolf optimizer(GWO), and improved SOA(ISOA) by using eight complex benchmark function optimization problems. Experimental results reveal that the proposed SFSOA has higher solution accuracy, faster convergence rate and stronger global search ability than its competitors and can effectively deal with complex function optimization problems. Finally, SFSOA is applied to solve the feature selection problems and the satisfactory results are obtained. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a new metaheuristic algorithm based on the behavior of the salps swarm. In view of the defects of SSA, such as poor exploitation and exploration ability, six kinds of inertia weights oscillated in accordance to the increase of iteration number were introduced into the optimization process of SSA algorithm so as to make it not falling into the local optimum in advance. In order to verify the effectiveness of the improved SSA, the original SSA and the improved SSA with oscillatory inertia weights are tested with 24 CEC test functions and the simulation experiments results are analyzed. The simulation results show the proposed algorithms based on six oscillating inertia weight are better than the original SSA, and improve its convergence speed and optimization accuracy. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of salp swarm algorithm (SSA), such as poor exploitation and exploration ability and low convergence accuracy, the equal amplitude and convergence periodic oscillation inertia weights were proposed to balance the exploration and exploitation ability of SSA. In order to verify the effectiveness of the improved SSA, the periodic oscillation convergence inertia weight based SSA (POCIW-SSA), moth flame optimization (MFO) algorithm, sine and cosine algorithm (SCA), grey wolf optimizer (GWO), seagull optimization algorithm (SOA) are used to optimize 24 test functions, and the statistical results are analyzed and compared. Finally, it is concluded that the improved SSA has better optimization ability. In addition, the improved SSA is applied to the design of pressure vessels, and the results show that POCIW-SSA also shows better performance in solving typical engineering problems. [ABSTRACT FROM AUTHOR]

Symbiotic Organisms Search (SOS) algorithm is characterized based on the framework of relationships among the ecosystem species. Nevertheless, it is suffering from wasteful discovery, little productivity, and slack convergence rate. These deficiencies cause stagnation at the local optimum, which is hazardous in deciding the genuine optima of the optimization problem. Backtracking Search Algorithm (BSA) is likewise another streamlining method for comprehending the non-direct complex optimization problem. Consequently, in the current paper, an endeavor has been made toward the expulsion of the downsides from the traditional SOS by proposing a novel ensemble technique called e-SOSBSA to overhaul the degree of intensification and diversification. In e-SOSBSA, firstly, the mutation operator of BSA with the self-adaptive mutation rate is incorporated to produce a mutant of population and leap out from the local optima. Secondly, the crossover operator of BSA with the adaptive component of mixrate is incorporated to leverage the entire active search regions visited previously. The suggested e-SOSBSA has been tested with 20 classical benchmark functions, IEEE CEC2014, CEC2015, CEC2017, and the latest CEC 2020 test functions. Statistical analyses, convergence analysis, and diversity analysis are performed to show the stronger search capabilities of the proposed e-SOSBSA in contrast with the component algorithms and several state-of-the-art algorithms. Moreover, the proposed e-SOSBSA is applied to find the optimum value of the seven problems of engineering optimization. The numerical investigations and examinations show that the proposed e-SOSBSA can be profoundly viable in tackling real-world engineering optimization problems. [ABSTRACT FROM AUTHOR]

The last 10 years have been the decade of autonomous vehicles. Advances in intelligent sensors and control schemes have shown the possibility of real applications. Deep learning, and in particular convolutional networks have become a fundamental tool in the solution of problems related to environment identification, path planning, vehicle behavior, and motion control. In this paper, we perform a comparative study of the most used optimization strategies on the convolutional architecture residual neural network (ResNet) for an autonomous driving problem as a previous step to the development of an intelligent sensor. This sensor, part of our research in reactive systems for autonomous vehicles, aims to become a system for direct mapping of sensory information to control actions from real-time images of the environment. The optimization techniques analyzed include stochastic gradient descent (SGD), adaptive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propagation (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The training of the deep model is evaluated in terms of convergence, accuracy, recall, and F1-score metrics. Preliminary results show a better performance of the deep network when using the SGD function as an optimizer, while the Ftrl function presents the poorest performances. [ABSTRACT FROM AUTHOR]

Arithmetic Optimization Algorithm (AOA) is a meta-heuristic algorithm. Its main idea is to use the distribution behavior of the four main mathematical operators of addition(A), subtraction(S), multiplication(M) and division(D). Chaotic mapping strategy was introduced into the optimization process of AOA. Firstly, ten chaotic maps are separately embedded into two parameter Arithmetic Optimization Accelerator (MOA) and Arithmetic Optimization Probability (MOP) that affect the exploration and balance of AOA so as to enhance its ergodicity and non-repeatability, and improve its convergence speed and accuracy. Then a combination test was carried out by embedding ten chaotic maps into MOA and MOP at the same time, and their advantages and disadvantages were compared with the chaotic maps embedded separately. 26 benchmark functions in CEC-BC-2017 are used to examine the performance of the proposed chaotic arithmetic optimization algorithm (CAOA). Finally, four engineering design issues are optimized, involving three-bar truss design problem, welded beam design problem, pressure vessel design problem and spring design problem. The experimental results reveal that CAOA can obviously solve the function optimization and engineering optimization problems. AOA based on the chaotic interference factors has the merit of balancing the exploration and exploitation in the optimization process and enhances the convergence accuracy. [ABSTRACT FROM AUTHOR]

Symmetry in a differential evolution (DE) transforms a solution without impacting the family of solutions. For symmetrical problems in differential equations, DE is a strong evolutionary algorithm that provides a powerful solution to resolve global optimization problems. DE/best/1 and DE/rand/1 are the two most commonly used mutation strategies in DE. The former provides better exploitation while the latter ensures better exploration. DE/Neighbor/1 is an improved form of DE/rand/1 to maintain a balance between exploration and exploitation which was used with a random neighbor-based differential evolution (RNDE) algorithm. However, this mutation strategy slows down convergence. It should achieve a global minimum by using 1000 × D, where D is the dimension, but due to exploration and exploitation balancing trade-offs, it can not achieve a global minimum within the range of 1000 × D in some of the objective functions. To overcome this issue, a new and enhanced mutation strategy and algorithm have been introduced in this paper, called DE/Neighbor/2, as well as an improved random neighbor-based differential evolution algorithm. The new DE/Neighbor/2 mutation strategy also uses neighbor information such as DE/Neighbor/1; however, in addition, we add weighted differences after various tests. The DE/Neighbor/2 and IRNDE algorithm has also been tested on the same 27 commonly used benchmark functions on which the DE/Neighbor/1 mutation strategy and RNDE were tested. Experimental results demonstrate that the DE/Neighbor/2 mutation strategy and IRNDE algorithm show overall better and faster convergence than the DE/Neighbor/1 mutation strategy and RNDE algorithm. The parametric significance test shows that there is a significance difference in the performance of RNDE and IRNDE algorithms at the 0.05 level of significance.

Slime Mould Algorithm (SMA) is a new meta-heuristics algorithm that is inspired by the behaviors of slime mould from nature. Due to its effective performance, SMA has shown its competitive performance among other meta-heuristics algorithms and has been used in many mathematical optimization and real-world problems. However, SMA tends to sink into local optimality and lacks the diversity of the population. Therefore, to cope with the drawbacks of the classical SMA, this paper proposes an improved SMA algorithm named CHDESMA. First of all, the chaotic maps methods have the characteristics of ergodicity and randomness, and we used chaotic maps methods to replace the original random initialization to improve the diversity of the algorithm, which is more suitable for exploring the potential areas in the early stage. Then, based on the superior searching ability of the differential evolution algorithm (DE), the crossover and selection operations of DE are applied to CHDESMA, and the position is updated by the combination of the original SMA operator and the mutation strategy of DE, which effectively avoids the algorithm falling into local optimum. CHDESMA was evaluated using CEC2014 and CEC2017 test suits and four real-world engineering problems. CHDESMA was compared with advanced algorithms and DE variants. The experimental results and statistical analysis indicate that CHDESMA has competitive performance compared with the state-of-the-art algorithms.

As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved. In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter $w$ and acceleration coefficients $c_{1},c_{2}$ , and introduces a controlling factor $\gamma $ based on chaos theory to adaptively adjust the range of chaotic search. This makes the algorithm have favorable adaptability, and then the particles cannot only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution. To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions. In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite. Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.

The sparrow search algorithm (SSA) has poor population diversity, falls into the local optimum easily and low solution accuracy of multi-dimensional functions when solving the optimal solution of the objective function. To solve these probems, the improved sparrows search optimization algorithm (ISSA) is proposed. Firstly, the population is initialized with the opposition-based learning strategy to increase the population diversity. Secondly, the step factor is dynamically adjusted to improve the solution accuracy of the algorithm. Finally, Levy strategy is introduced into the sparrow position update formula for reconnaissance and early warning to improve the algorithm􀆳s ability of global search and jumping out of local extremum. ISSA, SSA and other algorithms are tested and perform rank sum test on 8 test functions to evaluate the solution accuracy, and Wilcoxon rank sum test is carried out. The experimental results show that the ISSA has higher searching performance. Meanwhile, ISSA is applied to the spectrum allocation of cognitive radio, the experimental results show that ISSA has better system benefit and fairness than other algorithms, which verifies the feasibility of ISSA in practice. [ABSTRACT FROM AUTHOR]

The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, moving toward the other gorillas, following silverback gorillas and competing with silverback gorillas for females. However, like other Metaheuristic Algorithms, the GTO still suffers from local optimum, low diversity, imbalanced utilization, etc. In order to improve the performance of the GTO, this paper proposes a modified Gorilla Troops Optimizer (MGTO). The improvement strategies include three parts: Beetle-Antennae Search Based on Quadratic Interpolation (QIBAS), Teaching–Learning-Based Optimization (TLBO) and Quasi-Reflection-Based Learning (QRBL). Firstly, QIBAS is utilized to enhance the diversity of the position of the silverback. Secondly, the teacher phase of TLBO is introduced to the update the behavior of following the silverback with 50% probability. Finally, the quasi-reflection position of the silverback is generated by QRBL. The optimal solution can be updated by comparing these fitness values. The performance of the proposed MGTO is comprehensively evaluated by 23 classical benchmark functions, 30 CEC2014 benchmark functions, 10 CEC2020 benchmark functions and 7 engineering problems. The experimental results show that MGTO has competitive performance and promising prospects in real-world optimization tasks. [ABSTRACT FROM AUTHOR]

Structural weight design is essential and difficult in engineering structure optimization. The design is affected by many factors and belongs to the NP problem. Swarm intelligent algorithm provides a valid way to solve the NP problem. Grasshopper optimization algorithm (GOA) is a nature-inspired algorithm that mimics the swarming behaviors of grasshopper insects, but the original GOA has two main problems: the convergence rate is slow and the convergence accuracy is poor. We propose a novel grasshopper optimization algorithm (CV-GOA) consisting of chaos strategy and velocity perturbation mechanism to improve the performance of standard GOA. In CV-GOA, the initial artificial swarm is constructed by Logistic map to increase the diversity of the population and improve the feasibility of finding the global optimal solution; then a set of the velocity vector is introduced and the velocity perturbation mechanism is used to update the velocity of grasshoppers and disturbs the position of grasshoppers, it can improve the searching speed of the algorithm and help the algorithm jump out of the local optimal trap, and improve the optimization accuracy of the algorithm. Experiments are conducted on fifteen benchmark functions to test the accuracy and convergence rate of CV-GOA. Experiments show the proposed CV-GOA achieves higher precision and better convergence rate than other variants. In addition, three structural weight design problems are optimized by CV-GOA, they are cantilever beam design problem, pressure vessel design problem and speed reducer design problem. The results indicate structural weight is designed with superiority. It also proves the effectiveness and value of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Harris Hawk Optimization (HHO) algorithm is a new population-based and nature-inspired optimization paradigm, which has strong global search ability, but its diversified local search strategies easily make it fall into local optimum. In order to enhance its search mechanism and speed of convergence, an new improved HHO algorithm based on the inverse cumulative function operator of Cauchy distribution and tangent flight operator was proposed. The proposed two operators are used as scale factors to control the step size. The walk path of Cauchy inverse cumulative integral function shows that its trajectory step length is relative to the average, which can further enhance the search stability of the algorithm. The Tangent flight has the function of balanced exploitation and exploration, and enhances the convergence ability of the algorithm. In order to verify the performance of the proposed algorithm, the 30 benchmark functions of the 2017 Institute of Electrical and Electronic Engineers (IEEE) Conference on Evolutionary Computation (CEC2017) and two practical engineering design problems are adopted to carry out the simulation experiments. On the other hand, the covariance matrix adaptation evolutionary strategies (CMA-ES), arithmetic optimization algorithm (AOA), butterfly optimization algorithm (BOA), bat algorithm (BA), whale optimization algorithm (WOA), sine cosine algorithm (SCA), and the proposed HHO algorithms were used for comparison experiments. Simulation results show that the proposed the Cauchy-distribution and Tangent-Flight Harris Hawk Optimization (CTHHO) Algorithm has strong optimization capability. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper. The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm's exploration and exploitation capability. A comprehensive test suite of standard benchmark problems with different natures, i.e., unimodal/multimodal and separable/non-separable, was used to test the convergence power of the proposed CTbADE algorithm. Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness, solution quality, and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms, such as jDE, CoDE, and EPSDE algorithms. This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computational models with a high number of parameters to adjust during the problem-solving process. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Grey wolf optimizer (GWO) is a biologicalinspired optimization algorithm with the advantages of few parameters, robustness, and easy implementation. It is widely used to solve the function optimization problem. However, GWO can easily fall into local optimum and can suffer from premature convergence. In this study, an improved GWO with differential perturbation, called IGWO, is presented. First, a non-linear reduction strategy is used instead of the linear reduction strategy in GWO to update the convergence factor, which increases the global search capability of IGWO. In addition, a random differential perturbation strategy with strong exploitation capability is embedded in GWO to increase the diversity of the population and ensure the local exploitation capability of IGWO. Finally, IGWO is tested with 16 benchmark functions. The simulation results show that IGWO outperforms PSO, GSA, GWO, ALO, MFO, mGWO, DE-GWO, wd-GWO, and HGWO in terms of convergence accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

For last two decades, nature‐inspired metaheuristic algorithms together with their modified, improved, and hybrid versions have been gaining huge popularity in the field of optimization in solving continuous and complex real‐life optimization problems. In this work, a novel improved symbiosis organism search (SOS) algorithm, called self‐adaptive beneficial factor‐based improved SOS (SaISOS, in short) is suggested. The self‐adaptive benefit factors and a modified mutualism phase (called "Three‐way mutualism phase") have been introduced here to upgrade the performance of SOS algorithm. A random weighted reflection coefficient and a new control operator have also been introduced. To validate the proposed algorithm and to compare its performance with other state‐of‐the‐art algorithms, 15 IEEE‐CEC 2015 functions have been employed and the experimental results confirm that SaISOS provides competitive results on most occasions. Also, the proposed algorithm is used to solve five real‐world optimization problems. Considering the average output, it is observed that the proposed method performs significantly better in solving the real‐world problems compared to the alternative state‐of‐the art techniques considered in this work. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Civil Engineering & Architecture is the property of Journal of Beijing University of Civil Engineering & Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The optimization performance of the PSO algorithm is affected by the speed update formula. Too fast convergence speed may cause the algorithm to miss the global optimal solution; too slow convergence speed may cause the algorithm to fall into the local optimal solution. Aiming at this problem, this paper proposes a PSO optimization algorithm based on improved compression factor, namely FPSO. By introducing the compression factor equation, the speed iteration formula is improved, and the influence on the algorithm caused by the improper setting of the learning factor is reduced. The new adjustment mechanism not only ensures the convergence performance of the PSO algorithm, but also weakens the influence of the speed boundary on the algorithm. Finally, five classical functions are selected to test the performance of the algorithm. The test results show that, compared with the traditional PSO algorithm, the proposed algorithm improves the global convergence ability and shortens the convergence time. [ABSTRACT FROM AUTHOR]