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In this article, an Improved Particle Swarm Optimization (IPSO) is proposed for solving global optimization and hyperparameter optimization. This improvement is proposed to reduce the probability of particles falling into local optimum and alleviate premature convergence and the imbalance between the exploitation and exploration of the Particle Swarm Optimization (PSO). The IPSO benefits from a new search policy named group-based update policy. The initial population of IPSO is grouped by the k-means to form a multisubpopulation, which increases the intragroup learning mechanism of particles and effectively enhances the balance between the exploitation and exploration. The performance of IPSO is evaluated on six representative test functions and one engineering problem. In all experiments, IPSO is compared with PSO and one other state-of-the-art metaheuristics. The results are also analyzed qualitatively and quantitatively. The experimental results show that IPSO is very competitive and often better than other algorithms in the experiments. The results of IPSO on the hyperparameter optimization problem demonstrate its efficiency and robustness. [ABSTRACT FROM AUTHOR]

Summary: This article proposes an intellectual quorum sensing‐centered bacterial horde optimization (QBHO) exert for assessing and authenticating the optimization problems. Quorum detection realizes the finest place of the bacteria by apprehending the most awful point within the hunt area. This mechanism is utilized in this study for problem‐solving. With this bacterial behavior and location perceptive, the superlative optimum result is obtained. In this anticipated QBHO algorithm, the competence of bacteria is enhanced to be beneficial so that results can be enhanced. The fundamental group‐built optimization procedures for instance, particle swarm, ant cluster, biogeography‐built algorithm, simulated bee constellation procedure and typical bacterial herd optimization through the distinctive considerations remain simulated and then associated through the intended practice for its ratification of results with QBHO. The projected QBHO practice is exploited to ensue with the seven‐typical benchmark with unimodal and multimodal trial functions intended to prove its practicability and ideality. The realized findings imply that the suggested QBHO approach surpasses the deemed optimization procedures for complex standard functions. Furthermore, the intended procedure might be appropriate to several engineering challenges, notably intended for intricate concurrent optimization problems. [ABSTRACT FROM AUTHOR]

We propose a new chaotic krill herd (CKH) in terms of the recently developed krill herd (KH) algorithm, to solve global numerical optimization problems. In CKH, chaos characteristics are introduced into the KH so as to further enhance its global search ability. The elitism scheme is also applied to store the best krill during the process when updating the krill. This new approach can speed up the global convergence, while preserving the advantage of the standard KH, thus making the approach more feasible for a wider range of practical applications. Here, thirteen different chaotic maps are used to tune the time interval of the krill in the KH algorithm. Twenty-four standard benchmark functions are utilized to verify the effects of the CKH and it has been demonstrated that, in most cases, the performance of CKH with a proper chaotic map is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Highlights A new meta-heuristic algorithm, namely CKH is proposed for global optimization. 13 different chaotic maps are applied to tune the main parameter of the KH algorithm. The elitism scheme is applied to keep the best fitness krill. The CKH algorithm is compared with ten well-known methods.

Psychological research has demonstrated that humans can think unconsciously. Unconscious thought (UT) refers to cognitive or affective decision-related processes that occur beyond conscious awareness. UT processes are considered more effective in complex decision-making than conscious thought (CT). In addition, holistic representation plays a key role in UT and consists of a multimodal, value-related cognitive process. While the neural correlates of UT have recently been investigated, the holistic representation hypothesis of UT has not been confirmed. Therefore, in the present study, we aimed to further evaluate this hypothesis by utilizing two UT tasks (person and consumer-product evaluations) in conjunction with an improved functional magnetic resonance imaging (fMRI) experimental protocol. Participants evaluated four alternatives with 12 attributes each. In the UT condition, once the decision information had been presented, the participants completed a 1-back task for 120 s and evaluated each alternative, as well as an independent 1-back task in the absence of any decision information. We then performed regression analysis of the UT performance in both tasks. Our results revealed a positive correlation between performance in the UT task and the use of the anterior part of the precuneus/paracentral lobule in the person evaluation task and between performance and the posterior part of the precuneus, postcentral gyrus, middle occipital gyrus, and superior parietal lobule in the consumer-product evaluation task. The involvement of the precuneus area in both tasks was indicative of a multimodal, value-related process and is consistent with the features of holistic representation, supporting a central role for holistic representation in UT. Furthermore, the involvement of different precuneus subregions in the two UT tasks may reflect the task dependency of the key representation critical for advantageous UT.

Psychological research has demonstrated that humans can think unconsciously. Unconscious thought (UT) refers to cognitive or affective decision-related processes that occur beyond conscious awareness. UT processes are considered more effective in complex decision-making than conscious thought (CT). In addition, holistic representation plays a key role in UT and consists of a multimodal, value-related cognitive process. While the neural correlates of UT have recently been investigated, the holistic representation hypothesis of UT has not been confirmed. Therefore, in the present study, we aimed to further evaluate this hypothesis by utilizing two UT tasks (person and consumer-product evaluations) in conjunction with an improved functional magnetic resonance imaging (fMRI) experimental protocol. Participants evaluated four alternatives with 12 attributes each. In the UT condition, once the decision information had been presented, the participants completed a 1-back task for 120 s and evaluated each alternative, as well as an independent 1-back task in the absence of any decision information. We then performed regression analysis of the UT performance in both tasks. Our results revealed a positive correlation between performance in the UT task and the use of the anterior part of the precuneus/paracentral lobule in the person evaluation task and between performance and the posterior part of the precuneus, postcentral gyrus, middle occipital gyrus, and superior parietal lobule in the consumer-product evaluation task. The involvement of the precuneus area in both tasks was indicative of a multimodal, value-related process and is consistent with the features of holistic representation, supporting a central role for holistic representation in UT. Furthermore, the involvement of different precuneus subregions in the two UT tasks may reflect the task dependency of the key representation critical for advantageous UT. [ABSTRACT FROM AUTHOR]

We propose a new chaotic krill herd (CKH) in terms of the recently developed krill herd (KH) algorithm, to solve global numerical optimization problems. In CKH, chaos characteristics are introduced into the KH so as to further enhance its global search ability. The elitism scheme is also applied to store the best krill during the process when updating the krill. This new approach can speed up the global convergence, while preserving the advantage of the standard KH, thus making the approach more feasible for a wider range of practical applications. Here, thirteen different chaotic maps are used to tune the time interval of the krill in the KH algorithm. Twenty-four standard benchmark functions are utilized to verify the effects of the CKH and it has been demonstrated that, in most cases, the performance of CKH with a proper chaotic map is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Highlights A new meta-heuristic algorithm, namely CKH is proposed for global optimization. 13 different chaotic maps are applied to tune the main parameter of the KH algorithm. The elitism scheme is applied to keep the best fitness krill. The CKH algorithm is compared with ten well-known methods. [ABSTRACT FROM AUTHOR]

Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA. [ABSTRACT FROM AUTHOR]

A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO’s strong global search capability and LS’s fast convergence ability. This paper proposes an adaptive LS starting strategy by utilizing our proposed quasi-entropy index to address its key issue, i.e., when to start LS. The changes of the index as the optimization proceeds are analyzed in theory and via numerical tests. The proposed algorithm is tested on multimodal benchmark functions. Parameter sensitivity analysis is performed to demonstrate its robustness. The comparison results reveal overall higher convergence rate and accuracy than those of CLPSO, state-of-the-art particle swarm optimization variants. [ABSTRACT FROM AUTHOR]

When the basic glowworm swarm optimization (GSO) algorithm optimizes the multi-peak function, the solution accuracy is not high, the later convergence is slow. To solve these problems, the fluorescent factor is introduced to adaptively adjust the step length of the firefly, an adaptive step length firefly optimization algorithm is proposed, this algorithm is an improved self-adaptive step glowworm swarm optimization (ASGSO). In this algorithm, the behavior of glowworms are developed, the step size is dynamically adjusted by the fluorescent factor, the algorithm avoids falling into a local optimum and improves the optimization speed and accuracy. The simulation results show that the improved ASGSO can search for global optimization more quickly and precisely. [ABSTRACT FROM AUTHOR]

To change the single experiment mode of traditional English teaching,based on modal theory to the paper deals with English experimental education. It points out that the new English education mode is not only the development of modern communication mode,the needs of cultural diversification,but also the requirements of the reform of English experimental education and the improvement of teaching quality. It has important significance for students to cultivate their multiple read consciousness and ability. Based on the modal function,English education experiment measures are put forward to promote laboratory construction. English may have more experiment teaching and teaching evaluation modes. The experiment results are investigated,the results show a good achievement. [ABSTRACT FROM AUTHOR]

The highly multimodal Rastrigin test function is analyzed by deriving a new aggregated progress rate measure. It is derived as a function of the residual distance to the optimizer by assuming normally distributed positional coordinates around the global optimizer. This assumption is justified for successful ES-runs operating with sufficiently slow step-size adaptation. The measure enables the investigation of further convergence properties. For moderately large mutation strengths a characteristic distance-dependent Rastrigin noise floor is derived. For small mutation strengths local attraction is analyzed and an escape condition is established. Both mutation strength regimes combined pose a major challenge optimizing the Rastrigin function, which can be counteracted by increasing the population size. Hence, a population scaling relation to achieve high global convergence rates is derived which shows good agreement with experimental data.

The Genetic Algorithm (GA) needs many computation iterations in solving multimodal function extremums，so its running efficiency is too low when dealing with large-scale data，which greatly limits its practical application. The classical parallel platform Hadoop can improve the GA running efficiency to some extent，while the state-of-the-art parallel platform Spark can release much more parallelism of GA by realizing parallel crossover，mutation and other operations on each computing node. For the convenience of comparison, the GA solving multimodal function extremums are designed and implemented on single node, Hadoop and Spark，respectively. Experimental results show that? compared with single node platform and Hadoop platform, the Spark based implementation not only significantly reduces the running time but also effectively avoids the problem of premature convergence because of its powerful randomness, while dealing with large-scale samples. [ABSTRACT FROM AUTHOR]