Heuristic Factor Research Articles

A practical path planning method for optimal repair paths between multiple small-size defects

PurposeThis study aims to solve the problem of repair path planning between multiple small-size defects in the field of additive manufacturing (AM) repair by using Python-based ant colony algorithm (ACO). The optimal parameter combination scheme is obtained by discussing the influencing factors of parameters in the ACO.Design/methodology/approachThe effects of the information heuristic factor α, the expected heuristic factor ß and the pheromone volatile factor ρ on the simulation results were investigated by designing a three-factor and three-level orthogonal experiment. The fast convergence of ACO in finding the optimal solution of multiple small-size defect repair path problem is proved by comparing the simulation results with those of genetic algorithm (GA) on the same data set.FindingsThe ACO can effectively solve the repair path planning problem between multiple small-size defects by optimizing the parameters. In the case of 50 defect locations, the simulation results of the ACO with optimized parameters are 159.8 iterations and 3,688 average path lengths, while the GA has 4,027.2 average path lengths under the same data set and the same number of iterations, and by comparison, it is proved that the ACO can find the optimal solution quickly in the small-size defects repair path planning problem, which greatly improves the efficiency of defect repair.Originality/valueThe parameter-optimized ACO can be quickly applied to the planning problem of repair paths between multiple small-size defects in the field of AM repair, which can better improve the defect repair efficiency and reduce the waste of resources.

A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation

In social network applications, competitive influence propagation often exhibits a certain degree of time lag. In the scenario of positive and negative competitive propagation studied in this paper, under the premise that negative nodes are activated first, how to find a set of positive seed nodes to participate in competitive propagation is studied, aiming to minimize the spread of negative influence. In the current study, the time complexity of the improved algorithms based on greedy strategies is high, which limits their scope of application in practical scenarios; some heuristic algorithms achieve better scalability, but there is still much room for improvement. Therefore, this paper proposes a new method to solve the influence propagation problem of delayed competition, also known as a heuristic propagation factor evaluation algorithm (HeuPFE). The main process is as follows: (1) We build a shortest path snapshot for the nearest propagation region of negative nodes and reduce the search space of competing nodes. (2) Then, we construct a node propagation factor evaluation method based on this shortest negative-oriented node path snapshot to reduce computational complexity. Comparing results with those of traditional heuristic algorithms, we carry out experiments on real datasets and verify the effectiveness of the proposed method.

An improved ant colony optimization algorithm for two-sided U-type assembly line balancing problems

Aiming at the improvement of production system, a new method of two-sided U-type assembly line balance is proposed. Define the partition of a two-sided U-type assembly line and derive the formula for the task start time. The multi-objective model of two-sided U-type assembly line balance is established by minimizing a smoothing index, the number of positions and stations. Considering the dual influence of heuristic factors and pheromone information, the improved ant colony optimization algorithm is proposed. Combined with 37 examples, the improved ant colony optimization algorithm is used to solve the balancing problem of a two-sided straight line and a two-sided U-type assembly line, respectively. Compared with the results of other algorithms, it verifies that the model is reasonable and the improved ant colony optimization algorithm is effective. The balance effect of a two-sided U-type assembly line is better than that of a two-sided straight line, which means the length of the assembly line is shortened.

An Improved Hybrid Path Planning Algorithm in Indoor Environment

Abstract During the path planning of robots in the indoor unstructured complex environment, there are often problems such as unreachable target points, deflection in the planning process, and failure to avoid dynamic obstacles in time. To solve these problems, an improved hybrid indoor path planning algorithm was proposed, wherein the improved global path planning algorithm was effectually integrated with improved local path planning algorithm. Firstly, the heuristic factor of traditional A-Star algorithm was optimized, search range and nodes were reduced, and then the path generated by traditional A-Star algorithm for path planning was smoothed using the angle bisector tangent point method. Secondly, combining path and environment information, local path planning was undertaken by utilizing the improved artificial potential field algorithm, and the unreachable target points problem was addressed by adjusting the repulsive field parameters. Additionally, dynamic potential field function was constructed to make it have the ability to resolve dynamic obstacles. Finally, in the part of actual environment verification, a comparison was made in this paper to assess the performance of the traditional hybrid algorithm against the improved algorithm in terms of path planning. The consequences showed that, by the hybrid algorithm proposed in this paper, the path planning length was reduced by 10.3%, the running time was decreased by 12.5%, and 34 redundant nodes were eliminated. The consequences indicated that the hybrid algorithm can effectively address the indoor unstructured and complex path planning problems.

Examining the influence of source factors and content characteristics of influencers' post on consumer engagement and purchase intention: A moderated analysis

The current research investigates the impact of fashion influencers on consumer engagement and purchase intention. Guided by the heuristic systematic model (HSM), this paper identifies heuristic and systematic factors of an influencer's post that contributes to consumer engagement and purchase intention. Data collected from 471 respondents was studied through path analysis. The results reveal that consumer engagement mediates between source credibility, content characteristics and purchase intention. Furthermore, the findings indicate that product involvement moderates the relationship between source factors, content characteristics and consumer engagement. The study adds to the extant literature and provides managerial insights for the fashion industry by suggesting ways to choose the right influencer and build stronger relationships with consumers.

A novel hexagonal grid map model and regenerated heuristic factor based strategy for intelligent manufacturing system’s AGV path planning problem solving

In modern industry, flexible manufacturing systems play an indispensable role, and the precision and connectivity of map modeling are crucial for improving the efficiency and flexibility of AGV (Automated Guided Vehicle) transportation systems. Facing challenges such as spatial constraints, equipment failures, and unexpected task changes in real manufacturing environments, this paper proposes an innovative AGV path planning method based on hexagonal grid map modeling. This method significantly enhances the connectivity, sampling frequency, and safety of AGV path planning by replacing traditional square grids with hexagonal ones. Additionally, this study employs an improved ant colony algorithm combined with the hexagonal grid map for AGV path planning. The algorithm incorporates heuristic factors to effectively avoid local optima. Furthermore, by dividing the ant colony into odd and even groups and implementing a bidirectional search strategy, the comprehensive exploration capability of the algorithm is further enhanced. Experimental results show that compared to traditional square grids, the hexagonal grid reduces the optimal path length by 11.1%, and the application of heuristic factors further shortens the path length by 18.6%. The bidirectional search strategy also significantly reduces the number of iterations required by the algorithm, with a maximum reduction of 46.46%. In summary, this paper provides a new, efficient, stable, and energy-saving solution for AGV path planning systems, with thorough optimizations and improvements made in every aspect from map modeling to path planning algorithms.

Are Auditors Biased? The Effect of Anchoring and Adjustment Heuristics on Auditors’ Judgment

Objective –The objective of this paper is to illustrate one of the crucial factors that affect auditors in making their judgment which can lead to systematic error. This paper provides background on how heuristics and biases can affect the judgment of auditors to increase the awareness of the anchoring and adjustment heuristic on audit judgment.Methodology –This research is a 2 x 2 experimental study which analyzes the employment of anchoring and adjusting bias of auditor in the process of making their audit judgment. This research employed three (3) experimental questions on 100 auditors in Surabaya, Indonesia, with experiences ranging from 0-8 years.Results –The result indicates that is evident that anchoring and adjustment heuristic exist and affect auditors’ judgment. The findings also indicates that experience influence auditors’ judgment whereas gender does not have such effect on auditors’ judgment. However, experience and gender can moderate the effect of anchoring and adjustment heuristic on auditors’ judgment.Research limitations/implications –The experimental questions used were simplified abstractions compared to the complex analytical reviews auditors typically encounter. Additionally, the sample size, consisting of 100 auditors, may limit the generalizability of the findings. The study exclusively focused on differences in auditor judgment influenced by anchoring and adjusting, without exploring potential contributions from other heuristic factors. Lastly, gender was categorized broadly as male and female, without further differentiation based on specific gender characteristics.Novelty/Originality –Its originality lies in adding moderation variable of gender and experience. While prior research has explored the impact of behavior on audit judgment to enhance decision-making in auditing, none have investigated anchoring and adjustment heuristics while considering the moderating factors of gender and experience.

A hybrid similarity model for mitigating the cold-start problem of collaborative filtering in sparse data

Similarity is a vital component for neighborhood-based collaborative filtering (CF). To improve the quality of recommendation, many similarity methods have been proposed and analyzed in recent decades. However, nearly all traditional similarity methods and many advanced similarity methods only utilize corated items among users to compute their similarity, which provides limited information in cold-start/sparse scenarios and yields misleading results. In addition, although a few advanced hybrid similarity models consider items beyond corated items, which can partly mitigate the above limitation, they still have drawbacks, such as disregarding penalizing noncorated items that have many disadvantages. In this paper, we explore a new robust hybrid similarity model, namely Wasserstein distance-based CF (WCF) model, for mitigating the cold-start problem of CF in sparse data. Specifically, we measure item similarity via the Wasserstein distance, which can help circumvent the drawbacks in the Bhattacharyya coefficient and KL divergence that are used in the literature, and is thus more robust in a cold-start/sparse scenario. Besides, we further design a new multiplicative user similarity formula which identifies all noncorated items as a whole to prioritize the importance of corated items and impair the negative effects of noncorated items, which will also play an important role in a cold-start/sparse scenario. In addition, we also propose two novel heuristic similarity factors to impair the negative effects of popular users and items as supplements. We conduct extensive experiments on five real-world benchmark recommendation datasets to test WCF. The experimental results show the superiority of WCF over other existing similarity methods in cold-start/sparse scenarios.

Energy Efficient Quantum-Informed Ant Colony Optimization Algorithms for Industrial Internet of Things

One of the most prominent research areas in information technology is the Internet of things (IoT) as its applications are widely used such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-the-art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics such as residual energy of the network, network lifetime, and the number of live IoT nodes.

Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train

Optimal energy-efficient train operation optimization is one of the widely studied areas in transportation science, which can significantly reduce energy consumption that accounts for a large proportion of operating costs. In order to adapt to the complex and changeable railway line conditions such as gradient, slope length, and speed limit and avoid the error in tracking speed curve, an optimal driving strategy decision-making (ODSD) model is proposed in this paper. The model considers the non-fixed sequence of driving regimes, and the regimes are directly selected in the discrete micro-subsegments of an equal time-division pattern. To solve this model efficiently, an improved ant colony system algorithm with the difference edges (ACSd) is proposed, which takes the heuristic effect of the difference between the best solutions of two adjacent iterations, i.e., “the difference edges,” into account. Additionally, energy-efficient heuristic factor and speed heuristic factor are presented to balance energy saving and speed. The results demonstrate that ACSd performs better than the basic ant colony system algorithm in solving traveling salesman problem (TSP) and provides more flexible driving strategies for the ODSD model.

To Access or Not to Access: Influencing Factors for Government Microblogging Information Seeking and Avoidance among Chinese Generation Z

In recent years, the effective enhancement of information communication effect through governmental social media in has emerged as a universally concerned issue on government governance within the social media era. As Generation Z represents the primary and frequent users of social media, understanding the factors influencing their behaviors regarding information seeking and avoidance on governmental social media platforms is essential (in China, the principal governmental social media platform is government microblogging). Employing the grounded theory methodology, 31 participants of Chinese Generation Z were recruited for the present study, and data were collected using an in-depth interview. The results showed that the factors influencing the information-seeking behavior of Generation Z towards government microblogging mainly include heuristic seeking factors (personal preference, emotional value, and following hot topics), systematic seeking factors (task demand and expert recommendation), and defensive seeking factors (defending stance, authority seeking, and impression management). The factors influencing the information avoidance behavior of Generation Z towards government microblogging mainly include heuristic avoidance factors (clickbait titles, content layout, excessive length, and high redundancy), systematic avoidance factors (selective ignorance and terminological density), and resource-limited avoidance factors (demand scarcity, time scarcity, and vitality scarcity). This study contributes to a more comprehensive understanding of the government microblogging information behavior of Generation Z. Implications for government microblogging governance, strategy recommendations, and indicates directions for future research are discussed.

DP-A*: For Path Planing of UGV and Contactless Delivery

The unmanned logistics and distribution urgently require a large number of unmanned ground vehicles(UGVs) under the influence of the potential spread of the Coronavirus Disease 2019 (COVID-19). The path planning of UGV relies excessively on SLAM map, and has no self-optimization and learning ability for the space containing a large number of unknown obstacles. In this paper, a new dynamic parameter-A* (DP-A*) algorithm is proposed, which is based on the A* algorithm and enables the UGV to continuously optimize the path while performing the same task repeatedly. First, the original evaluation functions of the A* algorithm are modified by Q-Learning to memory the coordinates of unknown obstacle. Then, Q-table is adopted as an auxiliary guidance for recording the characteristics of environmental changes and generating heuristic factor to overcome the shortcoming of the A* algorithm. At last, the DP-A* algorithm can realize path planning in the instantaneous changing environment, record the actual situation of obstacles encountered, and gradually optimize the path in the task that needs multiple explorations. By several simulations with different characteristics, it is shown that our algorithm outperforms Q-learning, Sarsa and A* according to the evaluation criteria such as convergence speed, memory systems consume, Optimization ability of path planning and dynamic learning ability.

Strategic investments in entrepreneurial brand ventures by large incumbents

PurposeConsumer product firms renowned for marketing appear to be complementing brand creation, extension and acquisition with minority equity investments in entrepreneurial brand ventures (EBVs) for strategic purposes. Similarly, EBVs are looking for growth and resources that can be accessed via inter-organizational partnerships. This flourishing industry practice and the paucity of empirical research indicates the potential for new studies. The research objective was to examine why and how large incumbents were implementing strategic brand venturing (SBV), and with this understanding to develop a framework useful for descriptive and normative purposes.Design/methodology/approachThis qualitative research study comprised in-depth interviews and multiple data sources across seven case studies drawn from US subsidiaries of global firms within the consumer products industry. Grounded in resource theory, the dimensions of strategic brand equity investments are abductively derived.FindingsThe findings delineate 16 process capabilities within four aggregate clusters entailing, the designing of the SBV program, opportunity identification, brand entrepreneur partnerships and venture portfolio management. Prefaced by endogenous and exogenous antecedents, these process capabilities help to contribute strategic and financial value when implemented.Research limitations/implicationsThis qualitative research study yielded analytical rather than statistical generalizations. A range of market and economic factors exist in the United States contributing towards a favorable entrepreneurial and brand incubation climate. This may render the SBV concept as contingent and contextual. Furthermore, the view of brand entrepreneurs' regarding the design of the process model were not explicitly sought but inferred from the discourses of the venturing units interviewed.Practical implicationsThe article outlines several important implementation imperatives for corporations endeavoring to competitively advantage their brand portfolios via adoption of a minority equity investing strategy in EBVs. Practitioners are cautioned against myopically adopting this process alone as a success heuristic given other factors may impact success such as changes in corporate strategy or upper echelon sponsorship.Social implicationsMission preservation for social brand ventures being tethered to a large incumbent may need to be taken into account prior to and during SBV relationships.Originality/valueThe research contributes to the call for greater insights into the investment processes used in venturing relationships as well as coverage of new industry sectors beyond technology industries that often characterize corporate venture capital studies. Several novel findings emerged related to the importance of—the industry ecosystem; symbiosis between the founding brand entrepreneur and brand culture; synchronization of investment strategies with an emerging brand life-cycle model and serendipitous corporate entrepreneurial opportunities.

Elite-centered artificial bee colony algorithm with extended solution boundary

To improve the performance of Artificial Bee Colony (ABC) algorithm, we propose a novel approach (named ESOABC-EC&ACS). First, the existence of solutions outside the constrained region (ESO) is designed to explore the possibility of the search helped by these solutions, which facilitates exploring the boundary of the constrained region and obtaining optimal solutions faster. Then, an elite-centered search equation is proposed to balance exploration and exploitation. Finally, adaptive bee colony scaling is constructed to help convergence. Our approach is the combination of these three techniques. The experimental results demonstrate that ESOABC-EC&ACS outperforms other ABC variants and some non-ABC meta-heuristic algorithms on most of the test functions in terms of solution quality. And the proposed heuristic factor for the dynamic weapon target assignment problem can improve the optimization performance. The ESO is the first universal strategy proposed for the survival of solutions outside the constraint region (out-of-bounds) generated when solving various problems directly, which causes extended solution boundary and affects the exploration of the constrained region boundary.

Design of an improved intelligent optimization logistics path planning model for the background of the new industrial era

ABSTRACT In the context of the new industrial era, intelligent manufacturing, intelligent factories, and intelligent logistics have become hot topics during the industrial revolution. In order to meet the flexibility, flexibility, and efficiency of factory logistics in the era of Industry 4.0, and improve the punctuality of logistics distribution, a factory logistics path planning model is designed based on grid environment and Ant colony optimization algorithms. The algorithm optimizes and improves the search ability and adaptability of Ant colony optimization algorithms. The test results show that the Pheromone volatile number is 0.3, which is a moderate value. When the importance of Pheromone is 3 and the heuristic factor is 6, the average optimal cost and the average number of iterations are the minimum. As the variable increases, both evaluation indicators show a trend of decreasing first and then increasing. The improvement of the initial Pheromone can speed up the Rate of convergence of the algorithm, while the quality of the planned path results is better than that of the traditional planned path, and the shortest path length is reduced from 26.46 to 24.38. The path smoothed by B-spline curve is smooth and continuous. The algorithm proposed by the research institute converges quickly, with the shortest path value being 59.43, and the maximum reduction in path length reaching 18.44%. The optimized path length converges quickly after fluctuations, and the algorithm has strong adaptability to the environment. This model can achieve accurate logistics path planning, provide competitive solutions for the implementation of intelligent logistics in factories, and promote the 4.0 factory to build a truly intelligent factory.

The Effect of Heuristics on Indian Stock Market Investors: Investor Sentiment as a Mediator

This study analyses heuristic factors affecting investor sentiment and investment decision-making in the Indian stock market. A quantitative approach is formulated with the questionnaire design for the collection of primary data regarding investor sentiment, representativeness heuristic bias, overconfidence and availability bias. The data are collected from the structured questionnaires from individual investors. The collected data were analysed using partial least square structural equation modelling to examine the relationship between the constructs: investor sentiment, representativeness heuristic bias, overconfidence and availability bias. This paper provides empirical insights into the association of heuristic factors that positively influence investor sentiment and investment decision-making. It encourages investors to make reasonable choices and to control and understand their risk. The study sheds light on the impact of investor sentiment on investment decision-making and its antecedent. It shows that investors depend on sentiment when making investment choices. Policymakers should provide consumers with knowledge and appreciation of the correct stock choice; various campaigns considering the different groups of investors will benefit decision-makers and institutional financial practitioners. This paper allows investors to pick the proper investment assistance to avoid making wrong decisions that arise as an outcome of the attitude of investors.

High-Dimensional Feature Selection Based on Improved Binary Ant Colony Optimization Combined with Hybrid Rice Optimization Algorithm

In the realm of high-dimensional data analysis, numerous fields stand to benefit from its applications, including the biological and medical sectors that are crucial for computer-aided disease diagnosis and prediction systems. However, the presence of a significant number of redundant or irrelevant features can adversely affect system accuracy and real-time diagnosis efficiency. To mitigate this issue, this paper proposes two innovative wrapper feature selection (FS) methods that integrate the ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO). HRO is a recently developed metaheuristic that mimics the breeding process of the three-line hybrid rice, which is yet to be thoroughly explored in the context of solving high-dimensional FS problems. In the first hybridization, ACO is embedded as an evolutionary operator within HRO and updated alternately with it. In the second form of hybridization, two subpopulations evolve independently, sharing the local search results to assist individual updating. In the initial stage preceding hybridization, a problem-oriented heuristic factor assignment strategy based on the importance of the knee point feature is introduced to enhance the global search capability of ACO in identifying the smallest and most representative features. The performance of the proposed algorithms is evaluated on fourteen high-dimensional biomedical datasets and compared with other recently advanced FS methods. Experimental results suggest that the proposed methods are efficient and computationally robust, exhibiting superior performance compared to the other algorithms involved in this study.

A metaheuristic causal discovery method in directed acyclic graphs space

Causal discovery plays a vital role in the human understanding of the world. Searching a directed acyclic graph (DAG) from observed data is one of the most widely used methods. However, in most existing approaches, the global search has poor scalability, and the local search is often insufficient to discover a reliable causal graph. In this paper, we propose a generic metaheuristic method to discover the causal relationship in the DAG itself instead in of any equivalent but indirect substitutes. We first propose several novel heuristic factors to expand the search space and maintain acyclicity. Second, using these factors, we propose a metaheuristic algorithm to further search for the optimal solution closer to real causality in the DAG space. Theoretical studies show the correctness of our proposed method. Extensive experiments are conducted to verify its generalization ability, scalability, and effectiveness on real-world and simulated structures for both discrete and continuous models by comparing it with other state-of-the-art causal solvers. We also compare the performance of our method with that of a state-of-the-art approach on well-known medical data.

An investigation into various visualization tools for complex biological networks

Network biology has become crucial to understanding the complex structural characteristics of biological systems. Consequently, advanced visualization approaches are needed to support the investigation of such structures, and several network visualization tools have subsequently been developed to help researchers analyze intricate biological networks. While these tools support a range of analytical and interactive features, it is sometimes unclear to a data analyst or visualization designer which features are of most relevance to biologists. Thus, this study investigates and identifies essential factors for the visualization of complex biological networks using a mixed methodology approach. Based on the findings, essential factors were categorized as either generic and heuristic, where the former concern different analytical and interactive functionalities, such as an efficient layout, advanced search capabilities, plugin availability, graph analysis and user-friendliness, while the latter concern usability, such as information coding, flexibility, orientation and help.1Furthermore, the findings indicate that 12 of the 15 generic factors identified were moderately important, while all 10 heuristic factors identified herein were moderately important.

Digital humanism and artificial intelligence: the role of emotions beyond the human–machine interaction in Society 5.0

PurposeBy considering the challenges of Industry 5.0, the purpose of this study is to analyze the role of heuristic factors in the technical qualities and emotions of Millennials and Generation Z (Gen Z) to assess their acceptance of the use of artificial intelligence (AI) devices such as robots. For this purpose, this paper uses the innovative AI device use acceptance (AIDUA) framework. This research evaluates the implications of human–machine interactions for the usage of robots and AI in daily life.Design/methodology/approachThe proposed AIDUA model is tested using data collected from Millennials and Gen Z. First, a principal components analysis technique is used to validate each measure. Second, a multiple regression analysis using IBM SPSS 26.0 is conducted.FindingsThe results of this study suggest that human–machine interaction is a part of a complex process in which there are different elements determining individuals’ acceptance of the use of AI devices during daily life. This paper outlines both the theoretical and practical implications. This study enriches the AIDUA model by connoting it with features and emotions belonging to the younger generation. Additionally, this research offers technology companies suggestions for addressing future efforts on technical performance and on the alignments of the expectations of young people in Society 5.0.Originality/valueFirst, the originality of this paper lies in highlighting the binary role of emotions in triggering the use of AI devices and robots. Second, the focus on Millennials and Gen Z offers a new lens for the interpretation of longitudinal phenomena in the adoption of AI. Finally, the findings of this paper contribute to the development of a new perspective regarding a “heartly collaborative” approach in Society 5.0.