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Combined heat and power economic dispatch (CHPED) is one of the foremost subjects in the operation of power systems. In this article, a new variant of cuckoo search (CS) algorithm, elitist CS (yECS), is advanced to tackle CHPED. During the optimisation process of the original CS as well as lots of its variants, the guidance of search directions relies merely upon the best individual, causing the loss of other beneficial information and further influencing their performance potentials. Therefore, in yECS, an elitist mechanism is developed to fully utilise the beneficial information of other elite individuals. Specifically, three new iterative strategies are developed, one for the global search phase and the other two for the local search phase. Further, a coordinated mechanism is put forward to effectively integrate the local iterative strategies, thus helping yECS in maintaining an appropriate balance between exploitation and exploration. The superior performance of yECS is firstly substantiated via CEC 2017 test suite and two engineering design problems and then it is utilised to address CHPED problems. All optimal dispatch results acquired by yECS are feasible and in most cases display remarkable improvements over the results determined by other CS variants and some recently-published literature results. [ABSTRACT FROM AUTHOR]

To address the path planning problem for unmanned combat aerial vehicle (UCAV) more effectively, a novel two-stage path planning model is proposed. The first stage involves a longitudinal search primarily aimed at predicting the initial path, while the second stage is a horizontal search designed to correct the initial path. Furthermore, to tackle the UCAV path planning issue more effectively, this paper designs an improved elephant clan optimization (IECO) algorithm based on the average sample learning strategy, opposition-based learning, and Lévy flight disturbance strategy. Subsequently, IECO is integrated with the two-stage model (TSIECO) to address the UCAV path planning problem. Additionally, numerical experiments across 15 test functions reveal that IECO outperforms other algorithms in terms of optimization capability and convergence speed. Finally, the UCAV path planning experimental results indicate that the two-stage model based on IECO, as proposed in this paper, has significant advantages over traditional path planning models based on other swarm intelligence algorithms. Specifically, in three different simulated environments, the TSIECO has been tested on a total of 9 maps with varying parameters, yielding paths that are optimal in terms of cost and stability. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical theory of behavioral swarms, where the state of interacting entities, which are called active particles, includes in addition to position and velocity, an internal variable, called activity, which has the ability to interact with mechanical variables thus affecting the interaction rules. In turn, the mechanical variables can modify the dynamics of activity variable. This approach is useful for describing the dynamics of living systems with a finite number of interacting entities. This paper provides a general conceptual framework that extends the pioneering work [N. Bellomo, S.-Y. Ha and N. Outada, Towards a mathematical theory of behavioral swarms, ESAIM : Control Theory Var. Calculus 26 (2020) 125, https:/ /doi.org/10.1051/cocv/2020071]. The theory is firstly developed for the constant number of active particles. Then, it is considered for the case of particles tending to infinity. This theory is useful for describing the dynamics of living systems. Therefore, it provides the conceptual basis for developing a mathematical theory of behavioral swarms, which naturally lead to the study of a theory of swarm intelligence. A study of the dynamics of swarms shows how the theory can be applied to real world collective dynamics. [ABSTRACT FROM AUTHOR]

Why do human beings exhibit enduring personality differences, and to what extent are these differences shaped by biological and cultural evolution? Despite ongoing efforts, a consensus framework remains elusive. This paper introduces the Diversity Advantage Theory, partially shifting the focus from how personality impacts individuals to its influence within groups. Through an evolutionary lens, connections among personality, collective cognition, and group dynamics are explored, revealing that diversity in personality traits would tend to enhance collective intelligence and foster deliberation, mitigating group biases. The framework proposes that biologically evolved psychological mechanisms underlying personality, including individual niche-finding, adapt and conform to culturally evolving heuristic personality traits. This co-evolution stabilized the Big Few personality traits in western, educated, industrialized, rich, and democratic (WEIRD) societies, addressing recurring group challenges related to engagement, closure, task, risk, and change. The Diversity Advantage Theory offers a novel explanatory framework for stable personality traits, aligning with biology and testable in contemporary group contexts. At least three novel perspectives are introduced: (1) personality trait dimensions have emerged, in part, to establish normative behavior for functional group processes; (2) normative diversity along trait dimensions enhances collective intelligence; and (3) such diversity should also promote deliberation, mitigate group biases, and improve group decision-making. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid optimization algorithm named bat-salp swarm algorithm (BASSA). BASSA integrates the local exploitation capability of bat algorithm (BA) and the global exploration capability of salp swarm algorithm (SSA). Firstly, by introducing the echolocation of BA, the follower updating strategy of SSA is improved. Secondly, the algorithm selects between BA and SSA based on specific conditions. Finally, individuals undergo random differential mutation to increase population diversity, thereby avoiding local optima. To verify the effectiveness of the algorithm, we carry out experiments BASSA on 23 benchmark functions with different dimensions and compare it with 7 optimization algorithms, including BA, SSA, and 7 enhanced versions of SSA. Simulation results indicate that BASSA outperforms standard BA, SSA, and other enhanced algorithms in terms of mean and standard deviation. This suggests a significant improvement in optimization performance, with higher solution accuracy and faster convergence speed. Additionally, through performance evaluation on three real engineering problems, the results indicate that BASSA possesses strong optimization capabilities. [ABSTRACT FROM AUTHOR]

Software defect prediction is a crucial discipline within the software development life cycle. Accurate identification of defective modules in software can result in time and cost savings for developers. The ELM algorithm offers the benefits of rapid training and robust learning capabilities. Numerous researchers in the field of software defect prediction have employed the ELM algorithm. However, the ELM algorithm, a single hidden layer feedforward neural network, faces challenges related to random parameter selection and limited generalization ability. To enhance the predictive performance of the ELM algorithm in software defect prediction. Most researchers utilize swarm intelligence optimization algorithms to optimize extreme learning machines. However, these optimization methods may encounter challenges related to fall into local optimal solution. This paper introduces a new sparrow search algorithm (2SSSA) built upon the original sparrow search algorithm. To enhance the original sparrow algorithm's ability to escape local extrema, the pinhole imaging reverse learning and somersault foraging strategies are employed. The performance of 2SSSA in terms of optimization and convergence speed is assessed using 8 randomly selected benchmark functions and 8 CEC2017 functions. Additionally, ensemble learning is a prominent research focus in the field of software defect prediction. Ensemble learning is known for its ability to significantly enhance prediction performance and model generalization. As a result, the ELM optimized using 2SSSA serves as the foundational predictor in the bagging ensemble learning algorithm. We propose an ensemble algorithm for software defect prediction, denoted as 2SSEBA, which employs a 2-step optimization sparrow algorithm (2SSSA) to optimize extreme learning machines. Based on an evaluation of 25 publicly available software defect prediction datasets using 5 commonly employed metrics. The predictive performance of 2SSEBA significantly outperforms the other five advanced prediction algorithms. Furthermore, this conclusion is supported by both Friedman ranking and Holm's post-hoc test. [ABSTRACT FROM AUTHOR]

This study investigates the optimization of generation costs for thermal power plants in the South Sulawesi (Sulbagsel) electricity system in Indonesia. The novel swarm intelligence method, the horse herding optimization algorithm (HHOA), is inspired by the social behavior of horses within herds across different age groups. HHOA is a new metaheuristic algorithm recognized for its high efficiency in exploration and exploitation. The primary objective of the HHOA method is to minimize generation costs. To evaluate the effectiveness of the proposed method, similar swarm intelligence techniques, namely particle swarm optimization (PSO) and whale optimization algorithm (WOA), are also employed. Statistical analysis demonstrates that HHOA offers superior exploration and exploitation capabilities, along with strong consistency and accuracy. The optimization results for thermal generation costs during mid-day peak loads indicate that the PSO method reduces costs by 23.78%, the WOA method by 23.02%, while the HHOA-based method achieves a reduction of 24.23%. [ABSTRACT FROM AUTHOR]

This work introduces a new metaheuristic called attention-boundary algorithm (ABA). It offers a new approach in exploiting the boundaries of the space. The boundaries have 50 percent probability to be chosen as initial solution while the rest 50 percent probability is uniformly distributed within the space. Then, there is 50 percent probability that a solution is restarted at the boundaries if stagnation takes place. ABA employs three references for its directed searches which are the randomly solution within space, the middle of all better units plus the most optimal unit, and a randomly chosen unit within swarm. ABA is then implemented to find the optimal solution of 23 standard functions and four engineering design problems including pressure vessel, speed reducer, welded beam, and spring design problems. In this assessment, ABA is confronted with five metaheuristics including carpet weaver optimization (CWO), dollmaker optimization algorithm (DOA), hiking optimization (HO), total interaction optimization (TIA), and pufferfish optimization algorithm (POA). The result reveals that ABA is better than CWO, DOA, HO, TIA, and POA in 21, 17, 23, 18, and 16 functions consecutively in managing 23 standard functions. Meanwhile, ABA is on the best in handling speed reducer, third best in handling the pressure vessel design problem, and on the fourth best in handling spring and welded beam design problems. [ABSTRACT FROM AUTHOR]

The optimization algorithm known as Particle Swarm Optimization (PSO) is based on swarm intelligence and was created by modeling the foraging behavior of bird flocks. This study using the generalized regression neural network to improve particle swarm optimization (PSO) algorithm, proposed a target for cross-border electricity social network optimization algorithm PSO-PNNG, the simulation experiment in multiple real social network data environment and algorithm comparison, and the basic operation of genetic algorithm into the particle swarm algorithm, enhance the particle swarm optimization algorithm's performance, speed up the convergence speed. In this study, three social network datasets obtained by real reptiles were used to solve the proposed PSO-PNNG algorithm in a real social network data environment. The findings of the experiment indicate that the suggested multi-objective optimization algorithm for cross-border e-commerce social traffic network based on improved PSO has higher efficiency and accuracy than the traditional method. [ABSTRACT FROM AUTHOR]

As the most commonly used side-channel analysis method, Correlation Power Analysis (CPA) usually uses the divide-and-conquer strategy to guess the single-byte key in the scenario of block cipher parallel implementation. However, this method cannot effectively use the power consumption information, resulting in a large number of power consumption traces. Therefore, genetic algorithm-based CPA is proposed, which can efficiently extract keys by multi-byte power analysis. However, genetic algorithm-based CPA tends to sacrifice computational cost to achieve a high key guessing success rate. To solve the above problems, this article focuses on bitwise linear leakage and proposes a multi-byte power analysis architecture based on the raindrop ripple algorithm. First, we propose to complete the key initialization by multiple linear regression. Second, we propose a novel swarm intelligence algorithm, the raindrop ripple algorithm, tailored for multi-byte power analysis based on the principles of "family planning" and "eugenics," which greatly improves the probability of producing individuals with high fitness values. Third, we further enhance the possibility of the correct key being recovered by traversing the candidate key space in specific conditions. To verify the key guessing efficiency of the multi-byte power analysis architecture based on the raindrop ripple algorithm, comparative experiments are conducted on SAKURA-G with three power analysis methods based on genetic algorithms. Experimental results show that our proposal not only has the efficient power information utilization of multi-byte power analysis but also has a convergence speed comparable to or even faster than that of single-byte CPA. Its efficiency of key guessing is improved by 85.64% compared to EfficiencyGa-CPA, and its convergence speed is even faster than that of single-byte CPA at 725 power traces, and 83.87% faster than single-byte CPA at 1000 power traces, which is astonishing as a multi-byte power analysis. [ABSTRACT FROM AUTHOR]

Influence maximization is a critical research topic of social network analysis, particularly with the increasing involvement of individuals in the global networked society. The purpose of the problem is to identify k influential nodes from the social network and activate them initially to maximize the expected number of influenced nodes at the end of the spreading process. Although some meta-heuristics based on swarm intelligence or biological evolution have been proposed to tackle this intractable problem, further investigation is required to refine the exploration and exploitation operations based on the iterative information from the evolutionary process. In this paper, an adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies is proposed for the influence maximization problem. In order to enhance the evolutionary capability of the later stages of the discrete differential evolution, the mutation in the framework, consisting of three policies, namely comprehensive learning particle swarm mutation strategy, differential mutation strategy, and perturbation strategy, is implied based on different probabilistic models. An adaptive local search strategy is presented to improve the local optimum results based on a potential alternative library consisting of structural hole nodes, which guides the differential evolution to find a more optimal solution. Experimental results on six real-world social networks demonstrate the competitive performance of the proposed algorithm in terms of both efficacy and efficiency compared to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases. [ABSTRACT FROM AUTHOR]

In this research article, a fractional-order nonlinear Proportional plus Integral plus Derivative (FONPID) controller is incorporated into a complex surge tank system where the gains of the controller are tuned through a machine learning control approach to control the nonlinear variations in level setpoints. Obtaining a proper output response from a non-linear system is challenging and demanding for researchers. In this case, nonlinear Proportional plus Integral plus Derivative (NPID) and conventional Proportional plus Integral plus Derivative (PID) controllers are not sufficient for obtaining desired output robustness in the system performances. Hence, to fulfill the need for an adaptive controller for a spherical surge tank system, FONPID can be a better choice. The machine learning control is applied to the gains of the FONPID controller with the Cuckoo Search Optimization Algorithm (CSA), a swarm-intelligence algorithm mostly known for its levy flights and searching pattern for best quality eggs. The whole idea of using machine learning control is to tune the gains to make the controller adaptive towards parametric variation and uncertainties. The machine learning control uses the Integral of Absolute Error (IAE) performance index criteria as the minimum objective function of CSA for tuning of gain constraints of the controllers. The proposed FONPID controller is then compared with NPID and conventional PID controllers to stabilize level setpoint variations. The results demonstrate that the FONPID controller gives better, robust, and optimum results over NPID and PID controllers. In comparison to NPID and PID controllers, the FONPID controller performs significantly better, with gains ranging from 11.68% to 215.31% across various operational modes and system parametric variations. [ABSTRACT FROM AUTHOR]

With the proposal of intelligent mines, the demand for drilling is increasing daily. Therefore, it is particularly crucial to gather more geological data by interpolation of limited drilling data for subsequent three-dimensional geological modeling. In this paper, a hybrid sparrow optimization Kriging model (HSSA), in which chaos theory and Levy flight are integrated into the initial population update algorithm of the sparrow algorithm and the location update algorithm of the entrants, is proposed. Next, the golden sine optimization algorithm is introduced into the reconnaissance and early warning mechanism of the sparrow algorithm to further improve the accuracy and local escape ability. By the correlation optimization of the original sparrow algorithm, the speed and accuracy of swarm intelligence optimization are further improved. In addition, the model solves the parameters of the variation function of the ordinary Kriging interpolation and reduces the generation error of the formation data interpolation. The results of relevant experiments show that the hybrid sparrow optimization Kriging model improves the accuracy and convergence speed compared with other swarm intelligence algorithms and that the accuracy of this model is improved by 8.4% compared with the original Kriging interpolation algorithm. Based on the hybrid sparrow optimization Kriging model, we propose a three-dimensional stratigraphic model for the Yangchangwan Coal Mine, which provides further support for mining operations and three-dimensional stratigraphic research in this area. The accuracy and applicability of the hybrid sparrow optimization Kriging model are further explained using a case study with the stratigraphic model data in the Yangchangwan Coal Mine. HSSA with significant potential for applications in industries such as coal mining and geological exploration. In these fields, the efficient acquisition, processing, and modeling of stratigraphic data are critical for enhancing geological interpretation and optimizing operational workflows. [ABSTRACT FROM AUTHOR]

Since the advent of computational analysis and visualization of chemical compounds, Computer-Aided Drug Design has made significant contributions to drug discovery. Recently, de novo drug design and molecular optimization have garnered considerable attention. Traditional optimization methods often struggle with the discrete nature of molecular space, but evolutionary computations have demonstrated their versatility across various optimization problems, regardless of the nature of the objective functions. This paper introduces a novel evolutionary algorithm, the Swarm Intelligence-Based Method for Single-Objective Molecular Optimization. Several experiments were conducted to showcase the efficiency of the proposed method, which identifies near-optimal solutions in a remarkably short time. The results were then compared with those of other state-of-the-art methods in the field. [ABSTRACT FROM AUTHOR]

This article explores the intersection between cognition theories and urban planning, conceptualizing the city as a distributed socio-cognitive architecture. It traces the evolution of these theories through three waves—functionalism, social externalism, and radical enactivism —. Correspondingly, the article suggests implications for reorienting urban planning approaches, highlighting participatory design, collaborative placemaking, and the nurturing of place-based affordances. Drawing examples from existing planning literature, it demonstrates resonances with Extended Mind-informed orientations. The conclusion synthesizes these insights, proposing a potentially transformative framework by rethinking planning as more participatory, pluralistic, and cognitively integrative, challenging internalist and technocratic assumptions. [ABSTRACT FROM AUTHOR]

Workload migration among cloud data centers is currently an evolving task that requires substantial advancements. The incorporation of fuzzy systems holds potential for enhancing performance and efficiency within cloud computing. This study addresses a multi-objective problem wherein the goal is to maximize the interpretability and the percentage of renewable energy consumed by a fuzzy meta-scheduler system in cloud scenarios. To accomplish this objective, the present research proposes a novel approach utilizing a multi-objective Knowledge Acquisition with a Swarm Intelligence Approach algorithm. Additionally, it takes advantage of a framework built on CloudSim, which includes virtual machine migration capabilities based on an expert system. Furthermore, a hierarchical fuzzy system is employed to assess rule base interpretability, along with another multi-objective algorithm, named Non-dominated Sorting Genetic Algorithm II. The framework and hierarchical system are employed to perform various simulation results concerning renewable energy and interpretability, while the algorithms aim to enhance the system's performance and interpretability. Empirical results demonstrate that it is possible to improve the performance of cloud data centers while improving the interpretability of the corresponding fuzzy rule-based system. The proposed multi-objective algorithm shows comparable or superior performance to the genetic algorithm across diverse scenarios. The simulation results indicate that improvements in cloud data center performance can be achieved while enhancing system interpretability. The average improvement in the interpretability index ranges from 0.6 to 6%, with a corresponding increase in renewable energy utilization ranging from 5 to 6%. [ABSTRACT FROM AUTHOR]

The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field. [ABSTRACT FROM AUTHOR]

In engineering applications, optimal parameter design is crucial. While Slime Mould Algorithm (SMA) excels in parameter discovery under constrained conditions, it faces challenges in achieving global convergence and avoiding local opsecttimal traps in complex tasks. This paper introduces an enhanced variant of SMA, termed CCHSMA, which integrates a Chaotic Local Search (CLS) mechanism to improve initial population diversity and combines Covariance Matrix Adaptation (CMA) and Harris Hawks Optimization (HHO) strategies to enhance global search efficiency. CCHSMA aims to improve search quality and reduce the likelihood of getting trapped in local optima. We evaluated CCHSMA's effectiveness by benchmarking it against the standard SMA and its variants using 30 CEC2017 test functions, and compared its performance with seven notable meta-heuristic algorithms and ten advanced swarm intelligence variants. The experimental results demonstrate that CCHSMA outperforms the other algorithms tested on the benchmark functions. To further validate its practical utility, CCHSMA's performance was also benchmarked against leading algorithms in real-world engineering applications. This paper uses detailed statistical methods, including the Wilcoxon signed-rank test and the Friedman test, to validate the comparative results. Our findings show that CCHSMA outperforms other algorithms in solving complex engineering optimization problems such as tension/compression spring design, pressure vessel design, and three-bar truss design, proving to be a robust tool for complex engineering optimization. Its enhanced initial population diversity and improved global search efficiency are essential for effectively addressing diverse engineering challenges. [ABSTRACT FROM AUTHOR]

The evaluation process of the Fractional Order Model is as follows. To address the commonly observed issue of low accuracy in traditional situational assessment methods, a novel evaluation algorithm model, the fractional-order BP neural network optimized by the chaotic sparrow search algorithm (TESA-FBP), is proposed. The fractional-order BP neural network, by incorporating fractional calculus, demonstrates enhanced dynamic response characteristics and historical dependency, showing exceptional potential for handling complex nonlinear problems, particularly in the field of network security situational awareness. However, the performance of this network is highly dependent on the precise selection of network parameters, including the fractional order and initial values of the weights. Traditional optimization methods often suffer from slow convergence, a tendency to be trapped in local optima, and insufficient optimization accuracy, which significantly limits the practical effectiveness of the fractional-order BP neural network. By introducing cubic chaotic mapping to generate an initial population with high randomness and global coverage capability, the exploration ability of the sparrow search algorithm in the search space is effectively enhanced, reducing the risk of falling into local optima. Additionally, the Estimation of Distribution Algorithm (EDA) constructs a probabilistic model to guide the population toward the globally optimal region, further improving the efficiency and accuracy of the search process. The organic combination of these three approaches not only leverages their respective strengths, but also significantly improves the training performance of the fractional-order BP neural network in complex environments, enhancing its generalization ability and stability. Ultimately, in the network security situational awareness system, this integration markedly enhances the prediction accuracy and response speed. [ABSTRACT FROM AUTHOR]

This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm's global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm's efficiency and robustness. Ultimately, an extensive validation of MISWOA through "simulation + experimentation" approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms. [ABSTRACT FROM AUTHOR]

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective optimization issues in recent years. Their study has garnered a lot of attention since multi-objective optimization problems have a hard high-dimensional goal space. The black-winged kite optimization algorithm still suffers from the imbalance between global search and local development capabilities, and it is prone to local optimization even though it combines Cauchy mutation to enhance the algorithm's optimization ability. The heuristic optimization algorithm of the black-winged kite fused with osprey (OCBKA), which initializes the population by logistic chaotic mapping and fuses the osprey optimization algorithm to improve the search performance of the algorithm, is proposed as a means of enhancing the search ability of the black-winged kite algorithm (BKA). By using numerical comparisons between the CEC2005 and CEC2021 benchmark functions, along with other swarm intelligence optimization methods and the solutions to three engineering optimization problems, the upgraded strategy's efficacy is confirmed. Based on numerical experiment findings, the revised OCBKA is very competitive because it can handle complicated engineering optimization problems with a high convergence accuracy and quick convergence time when compared to other comparable algorithms. [ABSTRACT FROM AUTHOR]

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter's parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system's input and the IIR filter's output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value. [ABSTRACT FROM AUTHOR]

The wisdom of the crowd is the finding that aggregating the judgments of many people can lead to surprisingly accurate group judgments. Usually statistical methods are used to aggregate people's judgments, but there are advantages to using cognitive models instead. Crowd judgments based on cognitive modeling can (a) identify experts and amplify their judgments, (b) provide a representational structure for aggregating complicated multidimensional judgments, (c) debias judgments that are affected by heuristic cognitive processes or competitive social situations, and (d) diversify the crowd by incorporating predictions about judgments that have not been observed. Demonstrations of these advantages are provided in case studies involving ranking, probability estimation, and categorization problems. [ABSTRACT FROM AUTHOR]

Several constrained optimization problems have been adequately solved over the years thanks to the advances in the area of metaheuristics. Nevertheless, the question as to which search logic performs better on constrained optimization often arises. In this paper, we present Dual Search Optimization (DSO), a co-evolutionary algorithm that includes an adaptive penalty function to handle constrained problems. Compared to other self-adaptive metaheuristics, one of the main advantages of DSO is that it is able auto-construct its own perturbation logics, i.e., the ways solutions are modified to create new ones during the optimization process. This is accomplished by co-evolving the solutions (encoded as vectors of integer/real values) and perturbation strategies (encoded as Genetic Programming trees), in order to adapt the search to the problem. In addition to that, the adaptive penalty function allows the algorithm to handle constraints very effectively, yet with a minor additional algorithmic overhead. We compare DSO with several algorithms from the state-of-the-art on two sets of problems, namely: (1) seven well-known constrained engineering design problems and (2) the CEC 2017 benchmark for constrained optimization. Our results show that DSO can achieve state-of-the-art performances, being capable to automatically adjust its behavior to the problem at hand. [ABSTRACT FROM AUTHOR]

The Grey Wolf optimizer (GWO) is an efficient meta-heuristic algorithm based on swarm intelligence, inspired by the hierarchical structure and hunting behavior of natural wolf packs. Due to straightforward algorithm flow lightweight and ease of implementation, GWO has been extensively applied to address optimization problems in various area. However, the original GWO suffers from slow convergence and a tendency to get trapped in local optimal solutions. In this paper, we propose an improved variant of GWO called Decision-Enhanced Grey Wolf optimizer (DEGWO), which introduces a weight assignment to the decisions made by three head wolves (α, β, δ) and establishes a decision value evaluation mechanism. Additionally, in order to prevent excessive reliance on α, β and δ that may lead to reduced population diversity and premature convergence issues, we incorporate dimension learning-based hunting and adaptive frequency perturbation mechanisms into DEGWO. A rigorous multiple analysis of comparisons on 24 well-known standard test functions with six state-of-the-art heuristics and five novel GWO variants, demonstrates that DEGWO exhibits superior capabilities in global exploration. Furthermore, to validate its applicability in other application domains, the proposed DEGWO algorithm was employed to optimize four simulation design problems. [ABSTRACT FROM AUTHOR]

We are confronted with the concept of intelligence every day. Starting with human intelligence to artificial intelligence. Some animals are also attested to be intelligent based on specific problems they solve. We also come across terms such as swarm intelligence, emotional intelligence or even physical intelligence. But there is still a lack of a clear definition of what intelligence actually is and, in particular, how it could be measured. Intelligence tests that provide quantitative information have so far only been available from psychology and only for people. There is a lack of criteria for what makes a system an intelligent system. This became particularly clear with the question of whether generative AI, such as ChatGPT, can be considered intelligent at all. So can intelligence be derived from the cognitive abilities of a system, or is it ultimately decisive how these abilities come about? This paper suggests a definition of the term intelligence and suggests an explanation for what constitutes intelligence and to what extent intelligence is required to gain knowledge. And finally it is questioned whether artificial systems are intelligent and have any knowledge at all. [ABSTRACT FROM AUTHOR]

AbstractAdolescents are vulnerable to the exponential increase in fake news spread. Collective judgment on the quality of news has been an effective intervention. Collective Learning platform makes it possible to apply collective intelligence through simultaneous interaction in a large group, similar to social networks. The objective of this article is to analyze whether this platform effectively improves how adolescents confront fake news. 360 Spanish students aged 15–16 years old participated in the experiment confronting a fictitious case of fake news. The quantitative analysis using ANOVA demonstrated that there were significant positive changes for addressing fake news, both overall as well as in each of the participants. We discuss how these results confirm that collective intelligence improves digital literacy in the Internet challenges. [ABSTRACT FROM AUTHOR]

An integrated approach involving response surface methodology (RSM) and artificial neural network-ant-colony hybrid optimization (ANN-ACO) was adopted to develop a bioprocess medium to increase the yield of Bacillus cereus neutral protease under submerged fermentation conditions. The ANN-ACO model was comparatively superior (predicted r2 = 98.5%, mean squared error [MSE] = 0.0353) to RSM model (predicted r2 = 86.4%, MSE = 23.85) in predictive capability arising from its low performance error. The hybrid model recommended a medium containing (gL−1) molasses 45.00, urea 9.81, casein 25.45, Ca2+ 1.23, Zn2+ 0.021, Mn2+ 0.020, and 4.45% (vv−1) inoculum, for a 6.75-fold increase in protease activity from a baseline of 76.63 UmL−1. Yield was further increased in a 5-L bioreactor to a final volumetric productivity of 3.472 mg(Lh)−1. The 10.0-fold purified 46.6-kDa-enzyme had maximum activity at pH 6.5, 45–55 °C, with Km of 6.92 mM, Vmax of 769.23 µmolmL−1 min−1, kcat of 28.49 s−1, and kcat/Km of 4.117 × 103 M−1 s−1, at 45 °C, pH 6.5. The enzyme was stabilized by Ca2+, activated by Zn2+ but inhibited by EDTA suggesting that it was a metallo-protease. The biomolecule significantly clarified orange and pineapple juices indicating its food industry application. [ABSTRACT FROM AUTHOR]

The fitness of an organism can be strongly affected by the decisions that it makes throughout its lifetime. These decisions may be spatial (deciding where to go), temporal (deciding when to perform an action), or a mixture thereof. How organisms make spatial or temporal decisions should involve different mechanisms because of fundamental differences between the two. For example, time is irreversible, while animals can traverse space more freely. Making decisions together as a group can improve the accuracy of decisions (a form of collective intelligence). However, to date, almost all existing research has been on collective spatial decisions and, as a result, it is through this spatial lens that our intuition of collective decisions has developed. Understanding how individuals in groups make timing decisions is particularly relevant in a changing climate, where both the optimal time to perform actions and the cues used to time the action are changing. Studying collective intelligence in the context of timing decisions will reveal novel mechanisms that social animals across taxa (including humans) use, allowing us to predict the future of species in a changing world and to design new bio-inspired strategies. The past decade has witnessed a growing interest in collective decision making, particularly the idea that groups can make more accurate decisions compared with individuals. However, nearly all research to date has focused on spatial decisions (e.g., food patches). Here, we highlight the equally important, but severely understudied, realm of temporal collective decision making (i.e., decisions about when to perform an action). We illustrate differences between temporal and spatial decisions, including the irreversibility of time, cost asymmetries, the speed–accuracy tradeoff, and game theoretic dynamics. Given these fundamental differences, temporal collective decision making likely requires different mechanisms to generate collective intelligence. Research focused on temporal decisions should lead to an expanded understanding of the adaptiveness and constraints of living in groups. [ABSTRACT FROM AUTHOR]

Honey Badger algorithm (HBA) is an intelligent adaptive meta-heuristic optimization algorithm with few parameters, fast convergence and good convergence accuracy for single-objective problems. However, many real-world optimization problems involve multiple conflicting objectives that need to be optimized simultaneously. A new multi-objective Honey Badger algorithm is proposed to solve the combined economic and environmental power scheduling problem. The proposed MOHBA combines the HBA with the Pareto dominance principle to produce a non-dominated solution. It uses an external elite storage mechanism with congested distance ordering to maintain the diversity of the distribution during the evolution of the Pareto optimal solutions. Furthermore, a fuzzy decision strategy is used to select the best compromise solution from the obtained Pareto bound. Then, to validate the performance of the proposed MOHBA, 20 different benchmark test functions are used to test it against other multi-objective optimization techniques. Moreover, the method is implemented on the multi-objective CEEPD problem for the IEEE 30-bus 6 generator and IEEE 118-bus 14 generator systems. Various objective function s in a multi-objective optimization space is confirmed by comparative studies with minimization schemes and fuzzy decision strategies are utilized to achieve the best scheduling solution for energy and emissions savings. The predominance of the algorithm and its potentiality to handle CEEPD problem several other algorithms. [ABSTRACT FROM AUTHOR]

We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings. [ABSTRACT FROM AUTHOR]

An irregular heartbeat is known as cardiac arrhythmia. Cardiac arrhythmia occurs when electrical impulses that instruct the heart to beat malfunction. To save lives, arrhythmia must be detected early. Machine Learning (ML) is one of the fastest, cheapest, and easiest disease detection approaches. This study introduces an improved Naked Mole Rat Algorithm (RLNMRA) to select highly influential features in diagnosis and then uses various ML classifiers to train the model. The experimental results showed accuracies of 95.0%, 99.0%, 97.0%, 98.0%, 98.0%, 92.0%, and 99.0% with KNN, RF, GB, SVM, LR, DT, and GNB, classifiers, respectively. These results were obtained after removing invalid features from the UCI Arrhythmia dataset. The proposed method was also evaluated using recall, precision, and F score. The performance of the proposed model indicates that it can be used in various domains. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are increasingly utilized to solve complex optimization problems because they can efficiently explore large solution spaces. The moss growth optimization (MGO), introduced in this paper, is an algorithm inspired by the moss growth in the natural environment. The MGO algorithm initially determines the evolutionary direction of the population through a mechanism called the determination of wind direction, which employs a method of partitioning the population. Meanwhile, drawing inspiration from the asexual reproduction, sexual reproduction, and vegetative reproduction of moss, two novel search strategies, namely spore dispersal search and dual propagation search, are proposed for exploration and exploitation, respectively. Finally, the cryptobiosis mechanism alters the traditional metaheuristic algorithm's approach of directly modifying individuals' solutions, preventing the algorithm from getting trapped in local optima. In experiments, a thorough investigation is undertaken on the characteristics, parameters, and time cost of the MGO algorithm to enhance the understanding of MGO. Subsequently, MGO is compared with 10 original and advanced CEC 2017 and CEC 2022 algorithms to verify its performance advantages. Lastly, this paper applies MGO to four real-world engineering problems to validate its effectiveness and superiority in practical scenarios. The results demonstrate that MGO is a promising algorithm for tackling real challenges. The source codes of the MGO are available at https://aliasgharheidari.com/MGO.html and other websites. [ABSTRACT FROM AUTHOR]

In mining mineral resources, it is vital to monitor the stability of the rock body in real time, reasonably regulate the area of ground pressure concentration, and guarantee the safety of personnel and equipment. The microseismic signals generated by monitoring the rupture of the rock body can effectively predict the rock body disaster, but the current microseismic monitoring technology is not ideal. In order to address the issue of microseismic monitoring in deep wells, this research suggests a machine learning-based model for predicting microseismic phenomena. First, this work presents the random spare, double adaptive weight, and Gaussian–Cauchy fusion strategies as additions to the multi-verse optimizer (MVO) and suggests an enhanced MVO algorithm (RDGMVO). Subsequently, the RDGMVO-Fuzzy K-Nearest Neighbours (RDGMVO-FKNN) microseismic prediction model is presented by combining it with the FKNN classifier. The experimental section compares 12 traditional and recently enhanced algorithms with RDGMVO, demonstrating the latter's excellent benchmark optimization performance and remarkable improvement effect. Next, the FKNN comparison experiment, the classical classifier experiment, and the microseismic dataset feature selection experiment confirm the precision and stability of the RDGMVO-FKNN model for the microseismic prediction problem. According to the results, the RDGMVO-FKNN model has an accuracy above 89%, indicating that it is a reliable and accurate method for classifying and predicting microseismic occurrences. Code has been available at https://github.com/GuaipiXiao/RDGMVO. [ABSTRACT FROM AUTHOR]

The Equilibrium Optimiser (EO) has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems. Balancing the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO research. To alleviate these limitations, an EO variant named adaptive elite‐guided Equilibrium Optimiser (AEEO) is introduced. Specifically, the adaptive elite‐guided search mechanism enhances the balance between exploration and exploitation. The modified mutualism phase reinforces the information interaction among particles and local optima avoidance. The cooperation of these two mechanisms boosts the overall performance of the basic EO. The AEEO is subjected to competitive experiments with state‐of‐the‐art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test suite. Experimental results demonstrate that AEEO outperforms several well‐performing EO variants, DE variants, PSO variants, SSA variants, and GWO variants in terms of convergence speed and accuracy. In addition, the AEEO algorithm is used for the edge server (ES) placement problem in mobile edge computing (MEC) environments. The experimental results show that the author's approach outperforms the representative approaches compared in terms of access latency and deployment cost. [ABSTRACT FROM AUTHOR]

Motivated by the operational scenarios of lock scheduling, we propose a serial-lock chain navigation problem (SLCNP) modeled on the Three Gorges-Gezhouba Dam (TGGD) for the first time. Ship grouping, synchronized moving, and grouped waiting operations are integrated into the ship navigation process. A mixed integer programming (MIP) model that incorporates real-world constraints such as ship priority, service fairness, traffic flow equilibrium, and phased ship placement is presented to optimize ship throughput and ship stay time. To solve the SLCNP, a sort-pick strategy-based swarm intelligence algorithm (SPSSIA) framework is developed that integrates the characteristics of SLCNP through a hybrid multi-section encoding method and a two-stage heuristic decoding approach. A swarm intelligence evolution mechanism is used to improve the search ability and robustness of the framework. Several instances are generated based on real data to verify the correctness and effectiveness of the model and algorithm. Computational results demonstrate the applicability and effectiveness of the proposed SPSSIA. Further analysis of the experimental results indicates that the key impact factors significantly influence the navigational performance of the TGGD system. The results of this study will provide practical guidance for the operational processes of inland river hubs with comparable characteristics. [ABSTRACT FROM AUTHOR]

In this research, an original algorithm called 3S optimizer (3SO) is presented, which is inspired by swarm intelligence (SI) techniques. This novel proposed 3SO algorithm takes both the local exploitation and the global exploration into consideration, so it has satisfactory convergence speed and good solution accuracy. In order to evaluate the search ability of 3SO algorithm, this paper uses a set of famous test functions (including 23 functions of CEC-BC-2005 and 29 test functions of CEC-BC-2017). It proves that the 3SO algorithm has a great competitiveness with the most advanced optimization methods in terms of solution accuracy, convergence speed and stability from the experimental results. In order to fully demonstrate the practical application potential of the new technique, the 3SO algorithm is successfully applied to four engineering design problems. The simulation results show that the proposed 3SO algorithm can effectively deal with practical application problems. [ABSTRACT FROM AUTHOR]

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To address the issues of low efficiency and poor noise immunity in traditional active distribution network (ADN) fault location methods based on swarm intelligent optimization algorithms, this paper proposes a two-stage fault location method utilizing the COA-SVM model and cosine similarity. First, this paper constructs the fault signature database for the target distribution network by randomly simulating single- and multi-point faults using the fault current state equation. Next, this paper introduces the COA-SVM classification model, establishing the high-dimensional mapping relationship between the fault current direction matrix and the fault zones through model training. The well-trained COA-SVM classification model is used to identify the fault zones, which include the fault line segments. Finally, for each identified fault zone, this paper calculates the cosine similarity of the fault current direction information of adjacent line segments, accurately pinpointing the fault line segments by identifying mutation points of the cosine similarity. Using the modified IEEE 33 node test distribution network as an example, simulation results demonstrate that the proposed two-stage fault location method offers higher accuracy and resistance to signal interference compared to fault location methods based on swarm intelligence optimization algorithms. The COA-SVM classification model surpasses conventional models, achieving high accuracy and excellent noise resilience. It accurately identifies fault segments within the test distribution network with a remarkable 100% precision. Moreover, the accuracy of fault localization remains above 83% when the FTU encounters fewer than three abnormal signals. [ABSTRACT FROM AUTHOR]

Cognitive differences can catalyze social learning through the process of one-to-one social influence. Yet the learning benefits of exposure to the ideas of cognitively dissimilar others often fail to materialize. Why do cognitive differences produce learning from interpersonal influence in some contexts but not in others? To answer this question, we distinguish between cognition that is expressed—one's public stance on an issue and the way in which supporting arguments are framed—and cognition that is latent—the semantic associations that underpin these expressions. We theorize that, when latent cognition is obscured, one is more likely to be influenced to change one's mind on an issue when exposed to the opposing ideas of cognitively dissimilar, rather than similar, others. When latent cognition is instead observable, a subtle similarity-attraction response tends to counteract the potency of cognitive differences—even when social identity cues and other categorical distinctions are inaccessible. To evaluate these ideas, we introduce a novel experimental paradigm in which participants (a) respond to a polarizing scenario; (b) view an opposing argument by another whose latent cognition is either similar to or different from their own and is either observable or obscured; and (c) have an opportunity to respond again to the scenario. A preregistered study (n = 1,000) finds support for our theory. A supplemental study (n = 200) suggests that the social influence of latent cognitive differences operates through the mechanism of argument novelty. We discuss implications of these findings for research on social influence, collective intelligence, and cognitive diversity in groups. This paper was accepted by David Simchi-Levi, organizations. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00895. [ABSTRACT FROM AUTHOR]

It is intuitive and theoretically sound to combine experts' forecasts based on their proven skills, while accounting for correlation among their forecast submissions. Simpler combination methods, however, which assume independence of forecasts or equal skill, have been found to be empirically robust, in particular, in settings in which there are few historical data available for assessing experts' skill. One explanation for the robust performance by simple methods is that empirical estimation of skill and of correlations introduces error, leading to worse aggregated forecasts than simpler alternatives. We offer a heuristic that accounts for skill and reduces estimation error by utilizing a common correlation factor. Our theoretical results present an optimal form for this common correlation, and we offer Bayesian estimators that can be used in practice. The common correlation heuristic is shown to outperform alternative combination methods on macroeconomic and experimental forecasting where there are limited historical data. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Supplemental Material: The data file is available at https://doi.org/10.1287/mnsc.2021.02009. [ABSTRACT FROM AUTHOR]