Adaptive Heuristics Research Articles

Методы оптимизации и алгоритм маршрутизации в транспортной логистике

The article analyzes the key optimization and planning methods used for transport logistics. An algorithm using graph models for the formation of optimal transportation routes, taking into account the main logistical factors, is considered. In contrast to the classical routing problem for transport logistics, the authors use multifactorial assessment and adap-tive heuristics in the presented work. The heuristics proposed by the authors takes into account not only the distance, but also important logistic parameters, such as fuel costs and the time of passage of the route section. The applied mul-tifactorial assessment allows us to build a refined graph model and obtain optimal routes that take into account many factors, which increases efficiency, reduces costs and delivery time. The problem of multi-criteria route optimization solved by the authors has practical application for routing the delivery of goods from large marketplaces to pick-up points taking into account many factors. To solve the problem, the authors present a multi-factor route optimization method A* with adaptive heuristics. For instrumental support of logistics systems in the object-oriented programming language C#, an algorithm is implemented in the form of a library. The developed algorithm and software allow solving problems of optimization, planning and development of the transport and logistics services sector.

Behavioural Logic and its Role in Evolving Micro-foundations Concept in Strategic Management: A Literature Review

The paper fills the research gap concerning a theoretical background for the micro-foundations concept in strategic management. It aims to explore the role of behavioural logic in evolving the micro-foundations in strategic management. The method that has been used is a critical literature review of the following concepts: micro-foundations, the behavioural theory of the firm, behavioural economics, strategic management, multi-level approach, and methodological individualism. The overarching findings are as follows. The concepts within the behavioural theory of the firm as bounded rationality, attention, learning, mental limitations, seeking information, dynamic nature of aspiration, proceeding information, conflict of interests, social relationships, and cooperation are the background of categories at the individual level. The concepts as conflict of interests, team cooperation, attention in teams, conflict of interests, team cooperation, career path, and bounded rationality are the background of the categories at the team level. The concepts as bounded rationality, standardised practices and programmes, operational procedures, habits, learning, responses and dependence to environment, organisational heterogeneity, organisational structure, hierarchic power, and hierarchic decision-making are the background of the following at the organisational level. The concepts within behavioural economics as bounded rationality, heuristics, ecological rationality, aspiration levels, economic opportunism, and adaptive heuristics are the background of the categories at the individual level. The concepts as habits, cognitive individual limitations, ecological rationality, adaptive heuristics, and aspiration level are the background of the categories at the organisational level. Sixth, making decisions under uncertain and complex conditions is the antecedent of researching environmental categories like dynamics, complexity, and uncertainty.

Nonlinear Optimization and Adaptive Heuristics for Solving Irregular Object Packing Problems

We review and present several challenging model classes arising in the context of finding optimized object packings (OP). Except for the smallest and/or simplest general OP model instances, it is not possible to find their exact (closed-form) solution. Most OP problem instances become increasingly difficult to handle even numerically, as the number of packed objects increases. Specifically, here we consider classes of general OP problems that can be formulated in the framework of nonlinear optimization. Research experience demonstrates that—in addition to utilizing general-purpose nonlinear optimization solver engines—the insightful exploitation of problem-specific heuristics can improve the quality of numerical solutions. We discuss scalable OP problem classes aimed at packing general circles, spheres, ellipses, and ovals, with numerical (conjectured) solutions of non-trivial model instances. In addition to their practical relevance, these models and their various extensions can also serve as constrained global optimization test challenges.

Improved A* Navigation Path-Planning Algorithm Based on Hexagonal Grid

Navigation systems are extensively used in everyday life, but the conventional A* algorithm has several limitations in path planning applications within these systems, such as low degrees of freedom in path planning, inadequate consideration of the effects of special regions, and excessive nodes and turns. Addressing these limitations, an enhanced A* algorithm was proposed using regular hexagonal grid mapping. First, the approach to map modeling using hexagonal grids was described. Subsequently, the A* algorithm was refined by optimizing the calculation of movement costs, thus allowing the algorithm to integrate environmental data more effectively and flexibly adjust node costs while ensuring path optimality. A quantitative method was also introduced to assess map complexity and adaptive heuristics that decrease the number of traversed nodes and increase the search speed. Moreover, a turning penalty measure was implemented to minimize unnecessary turns on the planned paths. Simulation results confirmed that the improved A* algorithm exhibits superior performance, which can dynamically adjust movement costs, enhance search efficiency, reduce turns, improve overall path planning quality, and solve critical path planning issues in navigation systems, greatly aiding the development and design of these systems and making them better suited to meet modern navigation requirements.

Adaptive agents may be smarter than you think: unbiasedness in adaptive expectations

Abstract Experimental evidence shows that human subjects frequently rely on adaptive heuristics to form expectations but their forecasting performance in the lab is not as inadequate as assumed in macroeconomic theory. In this paper, we use an agent-based model (ABM) to show that the average forecasting error is indeed close to zero even in a complex environment if we assume that agents augment the canonical adaptive algorithm with a Belief Correction term which takes into account the previous trend of the variable of interest. We investigate the reasons for this result using a streamlined nonlinear macro-dynamic model that captures the essence of the ABM.

Non-clinical intuitions and adaptive heuristics in emergency care: A scoping review

BackgroundAcross a range of fields, including healthcare, heuristics are typically conceived as a source of bias and systematic error. However, research across the psychological and management sciences shows that intuition and heuristics are also vital sources of adaptive decision strategies, especially in complex, uncertain environments. The complexity of the emergency care environment marks this environment as one in which non-clinical intuitions and heuristics are likely to emerge and function as adaptive decision strategies. The aim of this study was to map and contextualize what is known about leadership and non-clinical intuitions and heuristics in emergency care. MethodsBased on a systematic search of the Pubmed, Scopus, Web of Science, MEDLINE and CINAHL electronic databases, we conducted a scoping review to map what is known about leadership and non-clinical intuitions and adaptive heuristics in emergency care. ResultsOf the 1219 articles retrieved, 9 met the inclusion criteria. Our review revealed four key findings. First, intuitions are used to make judgments about patients, caring for patients, and coordinating with colleagues. Second, although non-clinical intuitions are documented, non-clinical heuristics are rarely studied. Third, the literature is focused on nurses and silent on the use of non-clinical intuition and heuristics among medical doctors. Finally, professional cultures influence clinicians’ use of intuitive sense- and decision-making. ConclusionThis review highlights the dearth of research into non-clinical intuitions and heuristics in emergency care. Understood in the context of insights from the psychological and management sciences about intuitions and ‘smart’ heuristics as adaptive decision strategies, our findings point to new frontiers of research into leadership in emergency care.

Bounded rationality, enactive problem solving, and the neuroscience of social interaction.

This article aims to show that there is an alternative way to explain human action with respect to the bottlenecks of the psychology of decision making. The empirical study of human behaviour from mid-20th century to date has mainly developed by looking at a normative model of decision making. In particular Subjective Expected Utility (SEU) decision making, which stems from the subjective expected utility theory of Savage (1954) that itself extended the analysis by Von Neumann and Morgenstern (1944). On this view, the cognitive psychology of decision making precisely reflects the conceptual structure of formal decision theory. This article shows that there is an alternative way to understand decision making by recovering Newell and Simon's account of problem solving, developed in the framework of bounded rationality, and inserting it into the more recent research program of embodied cognition. Herbert Simon emphasized the importance of problem solving and differentiated it from decision making, which he considered a phase downstream of the former. Moreover according to Simon the centre of gravity of the rationality of the action lies in the ability to adapt. And the centre of gravity of adaptation is not so much in the internal environment of the actor as in the pragmatic external environment. The behaviour adapts to external purposes and reveals those characteristics of the system that limit its adaptation. According to Simon (1981), in fact, environmental feedback is the most effective factor in modelling human actions in solving a problem. In addition, his notion of problem space signifies the possible situations to be searched in order to find that situation which corresponds to the solution. Using the language of embodied cognition, the notion of problem space is about the possible solutions that are enacted in relation to environmental affordances. The correspondence between action and the solution of a problem conceptually bypasses the analytic phase of the decision and limits the role of symbolic representation. In solving any problem, the search for the solution corresponds to acting in ways that involve recursive feedback processes leading up to the final action. From this point of view, the new term enactive problem solving summarizes this fusion between bounded and embodied cognition. That problem solving involves bounded cognition means that it is through the problem solver's enactive interaction with environmental affordances, and especially social affordances that it is possible to construct the processes required for arriving at a solution. Lastly the concept of enactive problem solving is also able to explain the mechanisms underlying the adaptive heuristics of rational ecology. Its adaptive function is effective both in practical and motor tasks as well as in abstract and symbolic ones.

Behavioral analytics for myopic agents

Many multi-agent systems have a single coordinator providing incentives to a large number of agents. Two challenges faced by the coordinator are a finite budget from which to allocate incentives, and an initial lack of knowledge about the utility function of the agents. Here, we present a behavioral analytics approach for solving the coordinator’s problem when the agents make decisions by maximizing utility functions that depend on prior system states, inputs, and other parameters that are initially unknown. Our behavioral analytics framework involves three steps: first, we develop a model that describes the decision-making process of an agent; second, we use data to estimate the model parameters for each agent and predict their future decisions; and third, we use these predictions to optimize a set of incentives that will be provided to each agent. The framework and approaches we propose in this paper can then adapt incentives as new information is collected. Furthermore, we prove that the incentives computed by this approach are asymptotically optimal with respect to a loss function that describes the coordinator’s objective. We optimize incentives with a decomposition scheme, where each sub-problem solves the coordinator’s problem for a single agent, and the master problem is a pure integer program. We conclude with a simulation study to evaluate the effectiveness of our approach for designing a personalized weight loss program. The results show that our approach maintains efficacy of the program while reducing its costs by up to 60%, while adaptive heuristics provide substantially less savings.

How Well Do Reinforcement Learning Approaches Cope With Disruptions? The Case of Traffic Signal Control

Data-driven and machine-learning-based methods are increasingly used in attempts to master the challenges of the world. But are they really the best approaches to manage complex dynamical systems? Our aim is to gain more insights into this question by studying various popular reinforcement learning methods for traffic signal control, namely in disrupted scenarios characterized by significant, unpredictable variations. The results are expected to be relevant in subject areas ranging from traffic physics to transportation theory, from dynamics in networks to complex systems, from control theory to self-organization, and from adaptive heuristics to machine learning.

A Meta Heuristic Multi-View Data Analysis over Unconditional Labeled Material: An Intelligence OCMHAMCV

Artificial intelligence has been provided powerful research attributes like data mining and clustering for reducing bigdata functioning. Clustering in multi-labeled categorical analysis gives huge amount of relevant data that explains evaluation and portrayal of qualities as trending notion. A wide range of scenarios, data from many dimensions may be used to provide efficient clustering results. Multi-view clustering techniques had been outdated, however they all provide less accurate results when a single clustering of input data is applied. Numerous data groups are conceivable due to diversity of multi-dimensional data, each with its own unique set of viewpoints. When dealing multi-view labelled data, obtaining quantifiable and realistic cluster results may be challenge. This study provides unique strategy termed OCMHAMCV (Orthogonal Constrained Meta Heuristic Adaptive Multi-View Cluster). In beginning, OMF approach used to cluster similar labelled sample data into prototypes of dimensional clusters of low-dimensional data. Utilize adaptive heuristics integrate complementary data several dimensions complexity of computational analysis data representation data in appropriate orthonormality constrained viewpoint. Studies on massive data sets reveal that proposed method outperforms more traditional multi-view clustering techniques scalability and efficiency. The performance measures like accuracy 98.32%, sensitivity 93.42%, F1-score 98.53% and index score 96.02% has been attained, which was good improvement. Therefore it is proved that proposed methodology suitable for document summarization application for future scientific analysis.

Heuristics in the wild: exploring fund manager decisions through the COVID pandemic

PurposeThis paper aims to provide a novel explorative perspective on fund managers’ decisions under uncertainty. The current COVID pandemic is used as a unique reference frame to study how heuristics are used in institutional financial practice.Design/methodology/approachThis study follows a grounded theory approach. A total of 282 diverse publications between October 2019 and October 2020 for 20 German mutual funds are qualitatively analyzed. A theory of adaptive heuristics for fund managers is developed.FindingsFund managers adapt their heuristics during a crisis and this adaptive process flows through three stages. Increasing complexity in the environment leads to the adaption of simplest heuristics around investment decisions. Three distinct stages of adaption: precrisis, uncertainty and stabilization emerge from the data.Research limitations/implicationsThis study’s data is based on publicly available information. There might be a discrepancy between publicly stated and internal reasoning.Practical implicationsMoney managers can use the provided framework to assess their decision-making in crises. The developed adaptive processes of heuristics can assist capital allocators who choose and rate fund managers. Policymakers and regulators can learn about the aspects of investor decisions that their actions and communication address. Teaching can use this study to exemplify the nature of financial markets as adaptive systems rather than static structures.Originality/valueTo the best of the author’s/authors’ knowledge, this study is the first to systematically explore the heuristics of professional money managers because they navigate a large-scale exogenous crisis.

Identify Patterns in Online Bin Packing Problem: An Adaptive Pattern-Based Algorithm

Bin packing is a typical optimization problem with many real-world application scenarios. In the online bin packing problem, a sequence of items is revealed one at a time, and each item must be packed into a bin immediately after its arrival. Inspired by duality in optimization, we proposed pattern-based adaptive heuristics for the online bin packing problem. The idea is to predict the distribution of items based on packed items, and to apply this information in packing future arrival items in order to handle uncertainty in online bin packing. A pattern in bin packing is a combination of items that can be packed into a single bin. Patterns selected according to past items are adopted and periodically updated in scheduling future items in the algorithm. Symmetry in patterns and the stability of patterns in the online bin packing problem are discussed. We have implemented the algorithm and compared it with the Best-Fit in a series of experiments with various distribution of items to show its effectiveness.

Rethinking Rationality.

We seek to understand rational decision making and if it exists whether finite (bounded) agents may be able to achieve its principles. This aim has been a singular objective throughout much of human science and philosophy, with early discussions identified since antiquity. More recently, there has been a thriving debate based on differing perspectives on rationality, including adaptive heuristics, Bayesian theory, quantum theory, resource rationality, and probabilistic language of thought. Are these perspectives on rationality mutually exclusive? Are they all needed? Do they undermine an aim to have rational standards in decision situations like politics, medicine, legal proceedings, and others, where there is an expectation and need for decision making as close to "optimal" as possible? This special issue brings together representative contributions from the currently predominant views on rationality, with a view to evaluate progress on these and related questions.

Long-lasting heuristics principles for efficient investment decisions

PurposeBehavioral solutions to our cognitive biases have long been studied in the literature. However, there is still ample evidence of behavioral biases in decision-making, with only limited improvement in the medium/long term even when debiasing methods are applied. The purpose of this paper is to describe how financial investors could benefit from a proactive management of emotions combined with a set of learning and decision-making heuristics to make more efficient investments in the long run.Design/methodology/approachFirst, the authors offer a classification of the appropriate quantitative and qualitative methodologies to use in different ecological environments. Then, the authors offer a list of detailed heuristics to be implemented as behavioral principles intended to induce more long-lasting changes than the original rules offered by the adaptive toolbox literature. Finally, the authors provide guidelines on how to embed artificial intelligence and cognitive diversity within the investment decision architecture.FindingsImprovements in decision skills involve changes that rarely succeed through a single event but through a succession of steps that must be habitualized. This paper argues that implementing a more conscious set of personal and group principles is necessary for long-lasting changes and provides guidelines on how to minimize systematic errors with adaptive heuristics. To maximize their positive effects, the principles outlined in this paper should be embedded in an architecture that fosters cognitively diverse teams. Moreover, when using artificial intelligence, the authors advise to maximize the interpretability/accuracy ratio in building decision support systems.Originality/valueThe paper proposes a theoretical reflection on the field of behavioral research and decision-making in finance, where the chief goal is to offer practical advices to investors. The literature on debiasing cognitive biases is limited to the detection and correction of immediate effects. The authors go beyond the traditional three building blocks developed by the behavioral finance literature (search rules, stopping rules and decision rules) and aim at helping investors who are interested in finding long-term solutions to their cognitive biases.

The rationality of religion as adaptive heuristics

The rationality of religion as adaptive heuristics

An Adaptive Heuristic for Feeder Network Design with Optional Transshipment

An Adaptive Heuristic for Feeder Network Design with Optional Transshipment

Adaptive Energy-Aware Algorithms to Minimize Power Consumption and SLA Violation in Cloud Computing

Objective: With the establishment of virtualized datacenters on a large scale, cuttingedge technology requires more energy to deliver the services 24*7 hours. With this expansion and accumulation of information on a massive scale on data centers, the consumption of an excessive amount of power results in high operational costs. Therefore, there is an urgent need to make the environment more adaptive and dynamic, where the overutilization and underutilization of hosts are well known to the system and active measures can be taken accordingly. To serve this purpose, an energy-efficient method, for the detection of overloaded and under-loaded hosts, has been proposed in this paper. For implementing VM migration, VM placement decision has also been taken to save energy and reduce SLA (Service Level Agreement) rate over the cloud. Methods: In the paper, a novel adaptive heuristics approach has been presented that concerns with the utilization of resources for dynamic consolidation of VMs based on the mustered data from the usage of resources by VMs, while ensuring the high level of relevancy to the SLA. After identification of under-load and overload hosts, VM placement decision has been taken in the way that takes minimum energy consumption. A minimum migration policy has been adopted in the proposed methodology to minimize execution time. The validation of the effectiveness and efficiency of the suggested approach has been performed by using real-world workload traces in the CloudSim simulator. Results: The results shows that the proposed methodology is ideal for SLA, but costs more for VM migration. Conclusion: A deep analysis must be done in existing energy efficient approaches and a new platform should be suggested to save energy in real life.

A decomposition-based multi-objective genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem

In this paper, an efficient decomposition-based multi-objective genetic programming hyper-heuristic (MOGP-HH/D) approach is proposed for the multi-skill resource constrained project scheduling problem (MS-RCPSP) with the objectives of minimizing the makespan and the total cost simultaneously. First, the decomposition mechanism is presented to improve the diversity of solutions. Second, a single-list encoding scheme and an improved repair-based decoding scheme are designed to represent individuals and construct feasible schedules, respectively. Third, ten adaptive heuristics are developed elaborately to constitute a list of low-level heuristics (LLHs). Fourth, genetic programming is employed as the high-level heuristic (HLH) to generate a promising heuristics sequence from the LLHs set flexibly. Finally, the Taguchi method of design-of-experiment (DOE) is conducted to analyze the performance of parameter settings. The effectiveness of MOGP-HH/D is evaluated on a typical benchmark dataset and computational results exhibit the superiority of the proposed algorithm over the existing methods in solving multi-objective MS-RCPSP.

Heterogeneous adaptive heuristics for graph processing in Geo distributed Data Centre

Graph processing is an emerging computation model for a wide range of applications and graph partitioning is important for optimizing the cost and performance of graph processing jobs. In this paper, we propose a heterogeneous adaptive heuristics for geo-aware graph partitioning method which aims at minimizing the inter Data centre cost on data transfer time of graph processing jobs in geo-distributed DCs while satisfying the WAN usage budget. Heuristics can be multiple pass for graph processing. It is effective on assigning edges to different nodes. It adopts adaptive heuristics which address the challenges in WAN usage and network heterogeneities separately. Heuristics can be also applied to partition dynamic graphs minimize lightweight runtime overhead. Evaluation results show that proposed technique can reduce the inter-DC data transfer time by up to 78% and reduce the WAN usage by up to 80% compared to state-of-the-art graph partitioning methods with a low runtime overhead.

Adaptive Agents May Be Smarter than You Think: Unbiasedness in Adaptive Expectations

Agents forming adaptive expectations generally make systematic mistakes. This characterization has fostered the rejection of adaptive expectations in macroeconomics. Experimental evidence, however, shows that in complex environments human subjects frequently rely on adaptive heuristics – model-consistent expectations being simply too difficult or impossible to implement – but their forecasting performance is not as inadequate as assumed in the characterization above. In this paper we show that adaptive agents may not be as gullible as we used to think. In a model with adaptive expectations augmented with a Belief Correction term (which takes into account the drift of the macroeconomic variable of interest) the average forecasting error is frequently close to zero, hence (belief amended) adaptive expectations are close to unbiasedness.