Variable Search Research Articles

Global sensitivity analysis of nuclear district heating reactor primary heat exchanger and pressure vessel optimization

Recently, small modular reactors (SMRs) have received greater interest as a source for clean and affordable district heating (DH). Compared to power plants, the low-pressure, low-temperature design and nearly 100 % efficiency reduce the cost of produced energy considerably. However, few practical implementations exist yet, and cost estimates and design principles are subject to uncertainties whose interactions remain largely unknown. In this work, we present a techno-economic optimization and sensitivity analysis of a natural circulation DH SMR primary heat exchanger. A Cuckoo Search variant augmented with a modified Hooke-Jeeves search was used as the optimizer, with SimDec (simulation decomposition) subsequently employed for global sensitivity analysis. The reactor pressure vessel and containment vessel specific costs exhibited the greatest impact on the cost of heat and the optimized configurations. While low-pressure, low-temperature design is central to heating reactor cost-effectiveness, optimized primary circuit temperatures clearly exceeded previous assumptions. In a 5260 full-load hours mid-load application, a 34–41 €/MWh cost range was found for produced heat at 8 % interest and 20-year lifetime. For heat exchanger optimization, the results indicate the potential for considerable performance improvement from using deterministic local search for terminal convergence and sensitivity analysis for dimensionality reduction.

An adaptive operator selection cuckoo search for parameter extraction of photovoltaic models

Accurate, reliable, and efficient extraction of photovoltaic (PV) model parameters is an essential step towards PV system simulation, control, and optimization. Nevertheless, this problem is still facing great challenges because of its intrinsic nonlinear, multivariate, and multimodal properties. In this paper, a new variant of cuckoo search (CS), adaptive operator selection CS (AOSCS), is advanced for the PV model parameter extraction problems. AOSCS includes two major improvements: (1) an adaptive operator selection mechanism is developed to automatically assign the workloads of exploration and exploitation operators, and (2) the exploration and exploitation operators used in the original CS are modified to promote the exploration capability and reduce the blindness of search, respectively. The performance of AOSCS is firstly validated on CEC 2017 test suite and then it is utilized to solve the parameter extraction problems of five PV models. Moreover, further experiments on two commercial PV modules under distinct irradiance and temperature levels are also conducted to evaluate the practicality of the proposed algorithm. It is manifested that the results yielded by AOSCS are very competitive relative to other parameter extraction approaches. Accordingly, the proposed AOSCS is able to be served as an up-and-coming candidate algorithm for PV model parameter extraction problems.

Constrained inhomogeneous spherical equations: average-case hardness

In this paper we analyze computational properties of the Diophantine problem (and its search variant) for spherical equations $\prod_{i=1}^m z_i^{-1} c_i z_i = 1$ (and its variants) over the class of finite metabelian groups $G_{p,n}=\mathbb{Z}_p^n \rtimes \mathbb{Z}_p^\ast$, where $n\in\mathbb{N}$ and $p$ is prime. We prove that the problem of finding solutions for certain constrained spherical equations is computationally hard on average (assuming that some lattice approximation problem is hard in the worst case).

진보된 인터넷 발전효과가 스낵컬처에 미치는 영향

With the fast development of the lntemet in the late 20th century and the widespread adoption of smartphones, the importance of smartphones has increased significantly, becoming an integral part of daily life. This surge in smartphone usage has led to the emergence of various media and content, including the advent of “snack culture” and short-form content. While short-form content offers the advantage of quick and concise information consumption for media consumers, there is a downside of potential addiction due to easy access to infmmation. This paper focuses on the impact of the development of the Internet on the emergence of snack culture and its implications. The study categorizes the variables influenced by the advancement of the Internet into the usefulness and easy ues of smartphone adoption, the diversification of information search in terms of informativeness and immediacy, and the innovation and suitability of infmmation system development. It seeks to understand the causal relationships with the establishment of a multi-media environment and explores the satisfaction arising from the emergence of snack culture in various areas. The research involves data collection through surveys, and hypothesis testing is conducted using Smart PLS 4.0 and IBM SPSS version 23.0. The conclusions drawn are as follows: Firstly, the effects of the Internet development on smartphone adoption rates and the diversification of information search do not significantly impact the multi-media envirorunent, leading to the rejection of H1-1 and H1-2. Secondly, the impact of the Internet development on information system development is significant in the multi-media envirorunent, leading to the acceptance of H1-3. 1birdly, the multi-media envirorunent significantly influences the emergence of snack culture, supporting its adoption. The paper thus contributes to explodng the utilization of snack culture in various domains resulting from the effects of the Internet development.

Adaptive multi-scale Graph Neural Architecture Search framework

Graph neural networks (GNNs) have gained significant attention for their ability to learn representations from graph-structured data, in which message passing and feature fusion strategies play an essential role. However, traditional Graph Neural Architecture Search (GNAS) mainly focuses on optimization with a static perceptive field to ease the search process. To efficiently utilize latent relationships between non-adjacent nodes as well as edge features, this work proposes a novel two-stage approach that is able to optimize GNN structures more effectively by adaptively aggregating neighborhood features in multiple scales. This adaptive multi-scale GNAS is able to assign optimal weights for different neighbors in different graphs and learning tasks. In addition, it takes latent relationships and edge features into message passing into account, and can incorporate different feature fusion strategies. Compared with traditional ones, our proposed approach can explore a much larger and more diversified search space efficiently. We also prove that traditional multi-hop GNNs are low-pass filters, which can lead to the removal of important low-frequency components of signals from remote neighbors in a graph, and they are not even expressive enough to distinguish some simple regular graphs, justifying the superiority of our approach. Experiments with seven datasets across three graph learning tasks, including graph regression, node classification, and graph classification, demonstrate that our method yields significant improvement compared with state-of-the-art GNAS approaches and human-designed GNN approaches. Specifically, for example, with our framework, the MAE of the 12-layer AM-GNAS was 0.102 for the ZINC dataset, yielding over 25% improvement.

A Knowledge-Guided Multi-Objective Shuffled Frog Leaping Algorithm for Dynamic Multi-Depot Multi-Trip Vehicle Routing Problem

Optimization algorithms have a wide range of applications in symmetry problems, such as graphs, networks, and pattern recognition. In this paper, a dynamic periodic multi-depot multi-trip vehicle routing model for scheduling test samples is constructed, which considers the differences in testing unit price and testing capacity of various agencies and introduces a cross-depot collaborative transport method. Both the cost and the testing time are minimized by determining the optimal sampling routes and testing agencies, subjecting to the constraints of vehicle capacity, number of vehicles, and delivery time. To solve the model, a knowledge-guided multi-objective shuffled frog leaping algorithm (KMOSFLA) is proposed. KMOSFLA adopts a convertible encoding mechanism to realize the diversified search in different search spaces. Three novel strategies are designed: the population initialization with historical information reuse, the leaping rule based on the greedy crossover and genetic recombination, and the objective-driven enhanced search. Systematic experimental studies are implemented. First, feasibility analyses of the model are carried out, where effectiveness of the cross-depot collaborative transport is validated and sensitivity analyses on two parameters (vehicle capacity and proportion of the third-party testing agencies) are performed. Then, the proposed algorithm KMOSFLA is compared with five state-of-the-art algorithms. Experimental results indicate that KMOSFLA can provide a set of non-dominated schedules with lower cost and shorter testing time in each scheduling period, which provides a reference for the dispatcher to make a final decision.

Retraction: GerardDeepak, ArumugamSanthanavijayan. QGMS: A query growth model for personalization and diversification of semantic search based on differential ontology semantics using artificial intelligence. Comput Intell40: e12514, 2024 (10.1111/coin.12514)

The above article, published online on 08 March 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest‐edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. The authors have been informed of the decision to retract.

E2SCNet: Efficient Multiobjective Evolutionary Automatic Search for Remote Sensing Image Scene Classification Network Architecture.

Remote sensing image scene classification methods based on deep learning have been widely studied and discussed. However, most of the network architectures are directly reliant on natural image processing methods and are fixed. A few studies have focused on automatic search mechanisms, but they cannot weigh the interpretation accuracy and the parameter quantity for practical application. As a result, automatic global search methods based on multiobjective evolutionary computation have more advantages. However, in the ranking process, the network individuals with large parameter quantities are easy to eliminate, but a higher accuracy may be obtained after full training. In addition, evolutionary neural architecture search methods often take several days. In this article, in order to solve the above concerns, we propose an efficient multiobjective evolutionary automatic search framework for remote sensing image scene classification deep learning network architectures (E2SCNet). In E2SCNet, eight kinds of lightweight operators are used to build a diversified search space, and the coding connection mode is flexible. In the search process, a large model retention mechanism is implemented through two-step multiobjective modeling and evolutionary search, where one step involves the "parameter quantity and accuracy," and the other step involves the "parameter quantity and accuracy growth quantity." Moreover, a super network is constructed to share the weight in the process of individual network evaluation and promote the search speed. The effectiveness of E2SCNet is proven by comparison with several networks designed by human experts and networks obtained by gradient and evolutionary computing-based search methods.

General VNS for asymmetric vehicle routing problem with time and capacity constraints

We consider a real-life transportation problem from a hypermarket company in Serbia that consists of delivering online ordered and possibly perishable goods from a single depot to multiple customers. It can be modeled as an asymmetric vehicle routing problem that requires visiting and serving all customers using a limited number of homogeneous vehicles and taking into account time and capacity constraints. The objective function to be minimized is the total distance traveled. We provide a mixed-integer programming (MIP) formulation of the problem and develop several Local Search based metaheuristic methods exploring combinatorial formulation of the considered problem: Multistart Local Search (MLS), Greedy Randomized Adaptive Search Procedure (GRASP), and several variants of General Variable Neighborhood Search (GVNS) methods. All methods are compared on real-life instances provided by the hypermarket company and benchmark instances from the relevant literature. The obtained results show the superiority of GVNS-based methods with respect to both solution quality and running time, confirming that systematic search procedures have more success when dealing with hard optimization problems.

Rectangle Search: An Anytime Beam Search

Anytime heuristic search algorithms try to find a (potentially suboptimal) solution as quickly as possible and then work to find better and better solutions until an optimal solution is obtained or time is exhausted. The most widely-known anytime search algorithms are based on best-first search. In this paper, we propose a new algorithm, rectangle search, that is instead based on beam search, a variant of breadth-first search. It repeatedly explores alternatives at all depth levels and is thus best-suited to problems featuring deep local minima. Experiments using a variety of popular search benchmarks suggest that rectangle search is competitive with fixed-width beam search and often performs better than the previous best anytime search algorithms.

A descent subgradient method using Mifflin's line search for nonsmooth nonconvex optimization

We propose a descent subgradient algorithm for minimizing a function f : R n → R , assumed to be locally Lipschitz, but not necessarily smooth or convex. To find an effective descent direction, the Goldstein ε-subdifferential is approximated through an iterative process. The method enjoys a new two-point variant of Mifflin's line search in which the subgradients are arbitrary. Thus, the line search procedure is easy to implement. Moreover, in comparison to bundle methods, the quadratic subproblems have a simple structure, and to handle nonconvexity the proposed method requires no algorithmic modification. We study the global convergence of the method and prove that any accumulation point of the generated sequence is Clarke stationary, assuming that the objective f is weakly upper semismooth. We illustrate the efficiency and effectiveness of the proposed algorithm on a collection of academic and semi-academic test problems.

A VNS-based approach for solving the Manhattan metric straddle carrier routing problem with buffer areas

This paper presents a metaheuristic approach for solving an optimization problem that arises at container terminals where straddle carriers (SCs) transport containers between the stacking areas and the seaside. In such container terminals, operational efficiency depends mainly on SC routing. SCs routes should consider the order in which containers are unloaded and loaded at the quay cranes (QCs), taking into account the limited capacity of the buffer area of each QC where containers are temporarily stored after being handled by a QC or an SC. Besides the precedence relations (i.e., container sequences) and buffer capacities, the solution framework considers safety constraints. Efficient routing of SCs directly contributes to minimizing the idle time of QCs, thereby improving their overall productivity and minimizing the turnaround time of vessels, which is the objective of the problem. Specifically, we present two different variants of the Variable Neighborhood Search (VNS) algorithm. Each variant is initialized in both a greedy and a random manner. These algorithms address the problem by incorporating four LS operators commonly utilized in vehicle routing problems. We perform a comparative analysis of the results of these four approaches against each other and against solutions generated by an exact solver. Our numerical experiments show that the proposed algorithms perform better than the used solver, especially for bigger instances. A comparison with the results from the literature is also given and shows that the proposed VNS-based approach provides competitive results.

DOE-based multi-criteria optimization of starch/gly/CMC films’ composition and preparation procedure by casting deposition

Bioplastic materials represent a hot topic in the recent literature, with a particular focus on starch-based materials; despite the huge composite films proposed, bioplastics' weak points are still limiting their large-scale applications. In this work, we propose a DOE-based multi-criteria optimization of starch/gly/CMC films targeted to improve both film composition and lab-scale preparation. Film composition, starch source and CMC type are studied by mixture-process combined design to model and improve films’ tensile properties and solubility in water while the Plackett-Burman design is applied to screen between several casting procedure variants in search for an easy, reliable and reproducible lab-scale methodology. The chemometric approach allows us to identify the optimal composition to achieve both film flexibility and low solubility and to identify a successful and reproducible lab-scale preparation method.

Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search

Automated prediction systems based on machine learning (ML) are employed in practical applications with increasing frequency and stakeholders demand explanations of their decisions. ML algorithms that learn accurate sets of rules, such as learning classifier systems (LCSs), produce transparent and human-readable models by design. However, whether such models can be effectively used, both for predictions and analyses, strongly relies on the optimal placement and selection of rules (in ML this task is known as model selection). In this article, we broaden a previous analysis on a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific pre-existing LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated—in contrast to other LCSs where these tasks sometimes blend. We compare two baselines, random search and (1, lambda )-evolution strategy (ES), with six novelty search variants: three novelty-/fitness weighing variants and for each of those two differing approaches on the usage of the archiving mechanism. We find that random search is not sufficient and sensible criteria, i.e., error and generality, are indeed needed. However, we cannot confirm that the more complicated-to-explain novelty search variants would provide better results than (1, lambda )-ES which allows a good balance between low error and low complexity in the resulting models.

The time course of visual foraging in the lifespan: Spatial scanning, organization search, and target processing.

Visual foraging is a variant of visual search, consisting of searching for an undetermined number of targets among distractors (e.g., looking for various LEGO pieces in a box). Under non-exhaustive tasks, the observer scans the display, picking those targets needed, not necessarily all of them, before leaving the search. To understand how the organization of such naturalforaging tasks works, several measures of spatial scanning and organization have been proposed in the exhaustive foraging literature: best-r, intertarget distances, PAO, and target intersections. In the present study, we apply these measures and new Bayesian indexes to determine how the time course of visual foraging is organized in a dynamic non-exhaustive paradigm. In a large sample of observers (279 participants, 4-25 years old), we compare feature and conjunction foraging and explore how factors like set size and time course, not previously tested in exhaustive foraging, might affect search organization in non-exhaustive dynamic tasks. The results replicate previous findings showing younger observers' searching being less organized, feature conditions being more organized than conjunction conditions, and organization leading to a more effective search. Interestingly, observers tend to be less organized as set size increases, and search is less organized within a patch as it advances in time: Search organization decreases when search termination is coming, suggesting organization measures as potential clues to understand quitting rules in search. Our results highlight the importance of studying search organization in foraging as a critical source of understanding complex cognitive processes in visual search.

A Versatile Framework for Evaluating Ranked Lists in Terms of Group Fairness and Relevance

We present a simple and versatile framework for evaluating ranked lists in terms of Group Fairness and Relevance, in which the groups (i.e., possible attribute values) can be either nominal or ordinal in nature. First, we demonstrate that when our framework is applied to a binary hard group membership setting, our Group Fairness and Relevance (GFR) measures can easily quantify the overall polarity of each ranked list. Second, by utilising an existing diversified search test collection and treating each intent as an attribute value, we demonstrate that our framework can also handle soft group membership and that the GFR measures are highly correlated with a diversified information retrieval (IR) measure in this context as well. Third, using real data from a Japanese local search service, we demonstrate how our framework enables researchers to study intersectional group fairness based on multiple attribute sets. We also show that the similarity function for comparing the achieved and target distributions over the attribute values should be chosen carefully when the attribute values are ordinal. For such situations, our recommendation is to use multiple similarity functions with our framework: for example, one based on Jensen-Shannon Divergence (which disregards the ordinal nature of the groups) and another based on Root Normalised Order-aware Divergence (which has been designed specifically for handling ordinal groups). In addition, we highlight the fundamental differences between our framework and Attention-Weighted Rank Fairness (AWRF), a group fairness measure used at the TREC Fair Ranking Track.

An effective hybrid evolutionary algorithm for the clustered orienteering problem

In this paper, we study a variant of the orienteering problem called the clustered orienteering problem. In this problem, customers are grouped into clusters. A profit is associated with each cluster and is collected if and only if all customers in the cluster are served. A single vehicle is available to visit the customers. The goal is to maximize the total profits collected within a maximum travel time limit. To address this NP-hard problem, we propose the first evolutionary algorithm that integrates a backbone-based crossover operator and a destroy-and-repair mutation operator for search diversification and a solution-based tabu search procedure reinforced by a reinforcement learning mechanism for search intensification. The experiment results indicate that our algorithm outperforms the state-of-the-art algorithms from the literature on a wide range of 924 well-known benchmark instances. In particular, the proposed algorithm obtains new records (new lower bounds) for 14 instances and finds the best-known solutions for the remaining instances. Furthermore, a new set of 72 large instances with 50 to 100 clusters and at least 400 vertices is generated to evaluate the scalability of the proposed algorithm. Results show that the proposed algorithm manages to outperform three state-of-the-art COP algorithms. We also adopt our algorithm to solve a dynamic version of the COP considering stochastic travel time.

Retraction-Based Direct Search Methods for Derivative Free Riemannian Optimization

AbstractDirect search methods represent a robust and reliable class of algorithms for solving black-box optimization problems. In this paper, the application of those strategies is exported to Riemannian optimization, wherein minimization is to be performed with respect to variables restricted to lie on a manifold. More specifically, classic and linesearch extrapolated variants of direct search are considered, and tailored strategies are devised for the minimization of both smooth and nonsmooth functions, by making use of retractions. A class of direct search algorithms for minimizing nonsmooth objectives on a Riemannian manifold without having access to (sub)derivatives is analyzed for the first time in the literature. Along with convergence guarantees, a set of numerical performance illustrations on a standard set of problems is provided.

Semantic segmentation using Firefly Algorithm-based evolving ensemble deep neural networks

Automatic segmentation of salient objects in real-world images has gained increasing interests owing to its popularity in diverse real-world applications, such as autonomous driving, medical diagnosis, aviation security, and underwater surveillance. In this research, we propose Firefly Algorithm (FA)-enhanced evolving ensemble deep networks for semantic segmentation and visual saliency prediction. An improved FA model is proposed to optimize network hyper-parameters. Specifically, it employs mutation operators and a neighbouring search strategy with granular search steps to establish search intensification. It also emphasizes search diversification by adopting multiple dynamic hybrid leaders and diverse adaptive sine and cosine search trajectories in full and randomly selected sub-dimensions to overcome stagnation. Because of its competent segmentation performance, DeepLabV3+ is fine-tuned using transfer learning with FA-based hyper-parameter identification. We optimize the learning rate, momentum and weight decay of the transfer learning network. A number of optimized DeepLabV3+ networks with distinguishing learning configurations are yielded. An ensemble model is subsequently constructed by incorporating three optimized base networks to further strengthen segmentation performance. Evaluated using diverse challenging semantic segmentation and saliency prediction tasks using underwater and medical image data sets, our evolving ensemble deep network illustrates significant superiority over other state-of-the-art deep networks and existing studies. The proposed FA model also outperforms other search methods in solving diverse mathematical landscapes with statistical significance.

Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem.

This paper addresses a MinMax variant of the Dubins multiple traveling salesman problem (mTSP). This routing problem arises naturally in mission planning applications involving fixed-wing unmanned vehicles and ground robots. We first formulate the routing problem, referred to as the one-in-a-set Dubins mTSP problem (MD-GmTSP), as a mixed-integer linear program (MILP). We then develop heuristic-based search methods for the MD-GmTSP using tour construction algorithms to generate initial feasible solutions relatively fast and then improve on these solutions using variants of the variable neighborhood search (VNS) metaheuristic. Finally, we also explore a graph neural network to implicitly learn policies for the MD-GmTSP using a learning-based approach; specifically, we employ an S-sample batch reinforcement learning method on a shared graph neural network architecture and distributed policy networks to solve the MD-GMTSP. All the proposed algorithms are implemented on modified TSPLIB instances, and the performance of all the proposed algorithms is corroborated. The results show that learning based approaches work well for smaller sized instances, while the VNS based heuristics find the best solutions for larger instances.