Vast Solution Space Research Articles

Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights

The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various cancer types. Conventional feature selection methods often struggle to effectively navigate the vast solution space while maintaining high predictive accuracy. In response to these challenges, we introduce a novel feature selection approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed to enhance the performance of cancer classification tasks. Our proposed framework not only improves classification accuracy but also offers valuable insights into the underlying biological mechanisms driving cancer progression. Through comprehensive experiments conducted on real-world cancer datasets, including Central Nervous System (CNS), Leukemia, Breast, and Ovarian cancers, we demonstrate the efficacy of our method in identifying a smaller subset of unique and relevant genes. This selection results in significantly improved classification efficiency and accuracy. When compared with popular classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naive Bayes, our approach consistently outperforms these models in terms of both accuracy and F-measure metrics. For instance, our framework achieved an accuracy of 97.24% in the CNS dataset, 99.14% in Leukemia, 95.21% in Ovarian, and 87.62% in Breast cancer, showcasing its robustness and effectiveness across different types of cancer data. These results underline the potential of our RDO-XGBoost framework as a promising solution for feature selection in cancer data analysis, offering enhanced predictive performance and valuable biological insights.

A Novel Pareto-Optimal Algorithm for Flow Shop Scheduling Problem

Minimizing job waiting time for completing related operations is a critical objective in industries such as chemical and food production, where efficient planning and production scheduling are paramount. Addressing the complex nature of flow shop scheduling problems, which pose significant challenges in the manufacturing process due to the vast solution space, this research employs a novel multiobjective genetic algorithm called distance from ideal point in genetic algorithm (DIPGA) to identify Pareto-optimal solutions. The effectiveness of the proposed algorithm is benchmarked against other powerful methods, namely, NSGA, MOGA, NSGA-II, WBGA, PAES, GWO, PSO, and ACO, using analysis of variance (ANOVA). The results demonstrate that the new approach significantly improves decision-making by evaluating a broader range of solutions, offering faster convergence and higher efficiency for large-scale scheduling problems with numerous jobs. This innovative method provides a comprehensive listing of Pareto-optimal solutions for minimizing makespan and total waiting time, showcasing its superiority in addressing highly complex problems.

Probabilistic branch and bound considering stochastic constraints

In this paper, we investigate a simulation optimization problem that poses challenges due to (i) the inability to evaluate the objective and multiple constraint functions analytically; instead, we rely on stochastic simulation to estimate them, and (ii) a discrete and potentially vast solution space. Rather than providing a single optimal solution, our aim is to identify a set of near-optimal solutions within a specific quantile, such as the top 10%. Our investigation covers two different problem settings or frameworks. The first framework is focused solely on a stochastic objective function, disregarding any stochastic constraints. In this context, we propose employing a probabilistic branch-and-bound algorithm to discover a level set of solutions. Alternatively, the second framework involves stochastic constraints. To address such stochastically constrained problems, we utilize a penalty function methodology in conjunction with a probabilistic branch-and-bound algorithm. Furthermore, we establish a convergence analysis of both algorithms to demonstrate their asymptotic validity and highlight their theoretical properties and behavior. Our experimental results provide evidence of the efficiency of our proposed algorithms, showing that they outperform existing approaches in the field of simulation optimization.

Multi-objective multi-population simplified swarm optimization for container loading optimization with practical constraints

The container loading problem (CLP) holds significant importance in logistics and supply chain management due to its direct influence on transportation costs and times, thereby impacting the competitive advantages of enterprises across industries. Although there are existing studies for CLP, there remains a gap in addressing the optimization of multiple objectives while satisfying practical constraints. Moreover, container loading methods must also meet performance requirements such as high computational speed, explainability, and implementation capability. Although meta-heuristic-based algorithms have shown effective computational capabilities and performance, such algorithms often being trapped in the local optima especially when searching in the vast solution space of the CLP, particularly as the quantity and diversity of cargo sizes and shapes increase. Motivated by realistic needs, this study aims to develop a UNISON-based framework that integrates the merging spaces algorithm, a novel simple but effective hybrid simplified swarm optimization genetic algorithm (SSO-GA), and a multi-populations co-evolution strategy to determine the loading sequence of parcels to maximize space utilization and weight balance while satisfying to practical constraints. Specifically, the merging spaces algorithm merges fragmented small spaces resulting from loaded parcels into larger spaces, thereby facilitating the accommodation of additional parcels. The hybrid SSO-GA splits encoded solutions and updates segment using novel strategies resulting in the rearrangement of a group of parcels and their corresponding layouts for better space utilization. Furthermore, the multi-populations co-evolution strategy enhances the diversity of the search space and stabilizes solution quality by simultaneously exploiting the best solutions and exploring alternative solutions during the solution update process. An empirical study was conducted by using a public benchmark dataset which demonstrated the practical effectiveness of the proposed framework by significantly improving average space utilization while ensuring center balance and satisfying practical constraints. Furthermore, this study can also serve as a digital support system capable of assisting decision-makers in optimizing container loading operations, thereby improving productivity and saving valuable time.

A Critical Review of Inductive Logic Programming Techniques for Explainable AI.

Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence (AI) systems, explainable AI (XAI) has emerged as a response to improve modern machine learning algorithms' explainability. Inductive logic programming (ILP), a subfield of symbolic AI, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively leverages abductive reasoning to generate explainable first-order clausal theories from examples and background knowledge. However, several challenges in developing methods inspired by ILP need to be addressed for their successful application in practice. For example, the existing ILP systems often have a vast solution space, and the induced solutions are very sensitive to noises and disturbances. This survey paper summarizes the recent advances in ILP and a discussion of statistical relational learning (SRL) and neural-symbolic algorithms, which offer synergistic views to ILP. Following a critical review of the recent advances, we delineate observed challenges and highlight potential avenues of further ILP-motivated research toward developing self-explanatory AI systems.

Random Contrastive Interaction for Particle Swarm Optimization in High-Dimensional Environment

In high dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this paper designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-PSO, to tackle large-scale optimization problems (LSOPs) effectively and efficiently. Unlike existing interaction mechanisms for low-dimensional problems, RCI randomly chooses several different peers from the current swarm to construct a random interaction topology for each particle. Then, it lets the particle interact with the selected peers based on their current evolutionary information instead of their historical evolutionary information. Within the topology, RCI only propagates the evolutionary information of two contrastive dominators with the largest difference in fitness to direct the evolution of the particle. Therefore, particles with no more than two dominators in their topologies are not updated. Furthermore, a dynamic topology size adjustment scheme is devised to gradually enlarge the interaction topology. In this way, the swarm gradually switches from exploring the immense search space dispersedly to exploiting the found optimal regions intensively as the evolution continues. With these two strategies, RCI-PSO expectedly compromises search diversity and search convergence well at the swarm level and the particle level. At last, extensive experiments executed on two public LSOP suites verify that RCI-PSO performs competitively with or even much better than totally 40 state-of-theart large-scale approaches and preserves a good capability and scalability in tackling complex LSOPs.

Restructured electricity market strategies for the Indian utility system using support vector regression and energy valley optimizer

Restructuring the electricity market has led to the establishment of a competitive open market environment. The electricity market has introduced uncertainty and risk in the economic sector conventionally owned by the state. The power generated in the generating station is transferred to the distribution side in the power markets, which share a common transmission network. The power transfer will be in bulk amounts and needed to operate the electricity market securely and economically. The bulk amount of power transfer relies on accurately estimating Available Transfer Capability (ATC), representing the maximum allowable power flow through the existing transmission network while maintaining system reliability. The estimation of the ATC using a proposed hybrid method. The hybrid method comprises Repeated Power Flow (RPF) and Support Vector Regression (SVR) methods. The electricity market participants such as sellers and buyers submit bids to maximize their profit with the help of ATC values. The linear bid function is proposed to formulate participant strategies. Each participant will submit the availability of power requirement and willing price in the linear bid function. The Energy Valley Optimizer (EVO) algorithm is proposed to maximize the profit of each participant. The EVO algorithm efficiently explores a vast solution space, considering complex constraints and uncertainties inherent in the market dynamics to enhance economic gains. The proposed work is tested on the practical UPSEB (Uttar Pradesh State Electricity Board) 75-bus Indian utility system.

Optimizing Rack Locations in the Mobile-Rack Picking System: A Method of Integrating Rack Heat and Relevance

The flexible movement of racks in the mobile-rack picking system (MRPS) significantly improves the picking efficiency of e-commerce orders with the characteristics of “one order multi–items” and creates a challenging problem of how to place racks in the warehouse. This is because the placement of each rack in the MRPS directly influences the distance that racks need to be moved during order picking, which in turn affects the order picking efficiency. To handle the rack location optimization problem (RLOP), this work introduces a novel idea and methodology, taking into account the heat degree and the relevance degree of racks, to enhance the efficiency of rack placements in the MRPS. Specifically, a two-stage solution strategy is implemented. In stage 1, an integer programming model (Model 1) is developed to determine the heat and relevance degree of racks, and it can be solved quickly by the Gurobi. Stage 2 entails developing a bi-objective integer programming model (Model 2) with the objective to minimize the travel distances of robots in both heavy load and no-load conditions, using the rack heat and relevance degree as inputs. In light of the challenge of decision coupling and the vast solution space in stage 2, we innovatively propose two lower bounds by slacking off the distance between storage locations. A matheuristic algorithm based on Benders decomposition (MABBD) is designed, which utilizes Benders-related rules to reconstruct Model 2, introduces an enhanced cut and an improved optimal cut with RLOP characteristics, and designs the warm start strategy and the master variable fixed strategy. Given the substantial size of real-life problems, the Memetic algorithm (MA) is specifically devised to address them. Instances of varying sizes are also employed to validate the science and efficacy of the model and algorithm.

Research on Pricing and Replenishment Strategies of Supermarkets Based on PSO-LSTM- Transformer Models

The supermarket's role in ensuring market supply and enhancing livelihoods prompted the implementation of a new PSO-LSTM-Transformer hybrid model in this study. This amalgamation combines Particle Swarm Optimization (PSO), Long Short-Term Memory (LSTM), and Transformer to forecast commodity prices and inventory needs. By combining these methods, the research aimed to satisfy market requirements while maximizing grocery store profits and improving market operations sustainability.Particle Swarm Optimization (PSO) enhances prediction accuracy by effectively exploring the vast solution space for optimal solutions. Long Short-Term Memory (LSTM), which is known for capturing long-term data dependencies, enhances the comprehension and forecasting of market trends. Furthermore, the Transformer model enhances the forecasting process by capturing intricate patterns and relationships in market data through its attention mechanism.The research concludes that the PSO-LSTM-Transformer model effectively predicts commodity pricing and replenishment needs, thereby aiding supermarkets in making informed decisions to balance market demands and optimize revenue. The findings of the study contribute to the promotion of forecasting methods within the supermarket sector, facilitating efficient management of the market and sustainable economic growth.

Locating algorithm of steel stock area with asynchronous advantage actor-critic reinforcement learning

Abstract In the steel stockyard of the shipyard, the sorting work to relocate the steel plates already stacked to retrieve the target steel plate on the fabrication schedule is labor-consuming work requiring the operation of overhead cranes. To reduce the sorting work, there is a need for a method of stacking the steel plates in order of fabrication schedules when the steel plates arrive at the shipyard from the steel-making companies. However, the conventional optimization algorithm and heuristics have limitations in determining the optimal stacking location of steel plates because the real-world stacking problems in shipyards have vast solution space in addition to the uncertainty in the arrival order of steel plates. In this study, reinforcement learning is applied to the development of a real-time stacking algorithm for steel plates considering the fabrication schedule. Markov decision process suitable for the stacking problem is defined, and the optimal stacking policy is learned using an asynchronous advantage actor-critic algorithm. The learned policy is tested on several problems by varying the number of steel plates. The test results indicate that the proposed method is effective for minimizing the use of cranes compared with other metaheuristics and heuristics for stacking problems.

Ensemble deep learning enabled multi-condition generative design of aerial building machine considering uncertainties

The Aerial Building Machine (ABM) is a complex construction equipment used in high-rise building construction, facing challenges due to environmental uncertainties. This paper introduces a multi-condition generative design framework to improve ABM's visualization, operability, and intelligence. It seamlessly integrates real-time data between geometric and physical models and employs an ensemble deep learning model for objective value prediction, using a snapshot strategy. Combining structural reliability concepts with Latin hypercube sampling-based stochastic optimization, an optimal design scheme is obtained for uncertain loads. An ABM case study in China illustrates the approach's feasibility, showing it meets reliability requirements across different conditions and achieves significant improvements (16.59% under normal conditions and 16.91% under extreme wind conditions). Additionally, ensemble deep learning outperforms existing methods for ABM structural reliability estimation. Identifying optimal designs and evaluation options, this paper contributes a multi-condition optimization approach for enhanced structural reliability and establishes an efficient generative design workflow and system for exploring a vast solution space.

Core flow distribution optimization of a natural circulation reactor using genetic algorithm, simulated annealing and characteristic statistic algorithm

Optimization of core flow distribution is a critical issue in nuclear engineering, particularly for natural circulation reactors with limited driving force. By appropriately arranging the resistance at fuel assembly inlets, core flow distribution could be well matched with core power distribution, thereby reducing the non-uniformity of core outlet temperature distribution. This, in turn, could enhance the performance and safety of the reactor. However, the flow distribution optimization throughout the fuel life cycle is a complicated global optimization problem that entails a vast solution space. In this paper, the flow distribution optimization with minimizing the maximum-to-minimum outlet temperature difference as the primary optimization objective, has been carried out incorporating various meta-heuristic algorithms with the core thermal-hydraulic analysis code COBRA, for a 200 MW natural circulation light water reactor. Minimizing the maximum-to-average outlet temperature difference is also chosen as an optimization objective since a smaller maximum outlet temperature is desirable. After the parametric sensitivity analysis of the genetic algorithm, the simulated annealing, and the characteristic algorithm, a set of parameters of each algorithm for the current flow distribution optimization problem has been determined. The results show that different algorithms yield similar resistance schemes under both optimization objectives, and all exhibit satisfying optimization effects, among which simulated annealing converges to the global optimum with significantly less computational costs.

Spatial planning of urban communities via deep reinforcement learning.

Effective spatial planning of urban communities plays a critical role in the sustainable development of cities. Despite the convenience brought by geographic information systems and computer-aided design, determining the layout of land use and roads still heavily relies on human experts. Here we propose an artificial intelligence urban-planning model to generate spatial plans for urban communities. To overcome the difficulty of diverse and irregular urban geography, we construct a graph to describe the topology of cities in arbitrary forms and formulate urban planning as a sequential decision-making problem on the graph. To tackle the challenge of the vast solution space, we develop a reinforcement learning model based on graph neural networks. Experiments on both synthetic and real-world communities demonstrate that our computational model outperforms plans designed by human experts in objective metrics and that it can generate spatial plans responding to different circumstances and needs. We also propose a human-artificial intelligence collaborative workflow of urban planning, in which human designers can substantially benefit from our model to be more productive, generating more efficient spatial plans with much less time. Our method demonstrates the great potential of computational urban planning and paves the way for more explorations in leveraging computational methodologies to solve challenging real-world problems in urban science.

RuleDRL: Reliability-Aware SFC Provisioning With Bounded Approximations in Dynamic Environments

As a key enabling technology for 5G, network function virtualization abstracts services into software-based service function chains (SFCs), facilitating mission-critical services with high-reliability requirements. However, it is challenging to cost-effectively provide reliable SFCs in dynamic environments due to delayed rewards caused by future SFC requests, limited infrastructure resources, and heterogeneity in hardware and software reliability. Although deep reinforcement learning (DRL) can effectively capture delayed rewards in dynamic environments, its trial-and-error exploration in a vast solution space with massive infeasible solutions may lead to frequent constraint violations and traps in poor local optima. To address these challenges, we propose a RuleDRL algorithm that combines the capability of DRL to capture delayed rewards and the strength of rule-based schemes to explore high-quality solutions without violating constraints. Specifically, we first formulate the reliable SFC provision problem as an integer nonlinear programming problem, which is proven to be NP-hard. Then, we jointly design DRL and rule-based schemes that are coupled to make the final decision and establish a bounded approximation ratio in general cases. Extensive trace-driven simulations show that RuleDRL can save the total cost by up to 65.67% and improve the SFC acceptance ratio by up to 82%, compared to the state-of-the-art solution.

Intelligent Hybrid Trading Strategies Based on Quantum-Inspired Algorithm

Investing in stocks is a common choice for financial management. Technical indicators (TIs) assist investors in determining the best trading time to make a fortune. Moving average (MA) and relative strength index (RSI) are the most common TIs. The proposed hybrid technique maximizes the capabilities of these two indicators. This study utilizes the quantum-inspired algorithm to assist effectively in searching for the optimal solution in the vast solution space. The proposed trading system contains four innovative features. First, the traditional usage restriction of MA and RSI is removed to increase their potential effectiveness and identify the most profitable trading strategy. Second, this research proposes an innovative hybrid indicator (HI) that combines MA and RSI to simultaneously achieve both benefits. HI eliminates the restriction of employing a single indicator at once. Third, an efficient quantum-inspired algorithm, the Global-best-guided Quantum-inspired Tabu Search Algorithm with Quantum NOT Gate (GNQTS), effectively and efficiently explores optimized parameters. Fourth, this study proposes 60 sliding windows to determine the optimal period for training and testing. The investment targets include well-known indices: DJIA, S&P 500, NASDAQ Composite Index, and NYA, as well as high-reputation companies on the US stock market: DJIA components. By removing the restrictions imposed by these two indicators and the use of HI, the experiment results demonstrate that GNQTS can discover optimal parameters to generate higher returns than state-of-the-art methods and buy-and-hold (B&H) strategies. The proposed hybrid strategy provides for the promising prospect of quantum-inspired applications and the utilization of multiple indicators.

The Price is Right: Exploring Pricing of Digital Industrial Platforms

ABSTRACT Pricing is a significant component in determining digital platforms’ strategic and financial position. Informed decisions concerning how to price digital industrial platforms require an integrated and holistic view that organizes heterogeneous pricing options. Given the complexity of pricing, we build a taxonomy based on a systematic literature review to advance our understanding of key pricing characteristics as well as help researchers and practitioners navigate the vast solution space of decision options.

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

On the Evolution of the Optimal Design of WDS: Shifting towards the Use of a Fractal Criterion

Several researchers have proposed methodologies for addressing the problem of designing optimal water distribution systems. Metaheuristic approximations are studied the most due to the vast solution space. In search of reducing computational time, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been tested with retrofitting from the Optimal Power Use Surface (OPUS) methodology. A previous study demonstrates how OPUS significantly improves the results since it seeks to reduce energy losses in the network, in order to approximate minimum-cost designs using fewer hydraulic executions. However, more research is still needed to determine applicable hydraulic criteria that allow an enhanced comprehension of optimal designs. Therefore, this paper aims to understand the characteristics of near-optimal solutions using designs from the retrofitted OPUS/NSGA-II Pareto fronts of four distinct networks (Hanoi, Balerma, Fossolo, and Modena). Moreover, fractal characteristics of the networks’ energy dissipation, flow, and diameter distribution have been analyzed for this purpose. In this way, outcomes suggest that the hydraulic gradient line box dimension in optimal designs approaches a value of two, demonstrating that objects resemble a single-plane surface. These promising results propose fractal analysis as a practical design criterion due to its hydraulic significance and low computational cost.

Pareto optimal metabolic engineering for the growth-coupled overproduction of sustainable chemicals.

Our research aims to help industrial biotechnology develop a sustainable economy using green technology based on microorganisms and synthetic biology through two case studies that improve metabolic capacity in yeast models Yarrowia lipolytica (Y. lipolytica) and Saccharomyces cerevisiae (S. cerevisiae). We aim to increase the production capacity of beta‐carotene (β‐carotene) and succinic acid, which are among the highest market demands due to their versatile use in numerous consumer products. We performed simulations to identify in silico ranking of strains based on multiple objectives: the growth rate of yeast microorganisms, the number of used chromosomes, and the production capability of β‐carotene (for Y. lipolytica) and succinate (for S. cerevisiae). Our multiobjective optimization methodology identified notable gene deletions by searching a vast solution space to highlight near‐optimal strains on Pareto Fronts, balancing the above‐cited three objectives. Moreover, preserving the metabolic constraints and the essential genes, this study produced robust results: seven significant strains of Y. lipolytica and seven strains of S. cerevisiae. We examined gene knockout to study the function of genes and pathways. In fact, by studying the frequently silenced genes, we found that when the GPH1 gene is knocked out in S. cerevisiae, the isocitrate lyase enzyme is activated, which converts the isocitrate into succinate. Our goals are to simplify and facilitate the in vitro processes. Hence, we present strains with the least possible number of knockout genes and solutions in which the genes are turned off on the same chromosome. Therefore, we present results where the constraints mentioned above are met, like the strains where only two genes are switched off and other strains where half of the knockout genes are on the same chromosome. This study offers solutions for developing an efficient in vitro mutagenesis for microorganisms and demonstrates the efficiency of multiobjective optimization in automatizing metabolic engineering processes.

Probe Machine Based Computing Model for Maximum Clique Problem

Probe machine (PM) is a recently reported mathematic model with massive parallelism. Herein, we presented searching the maximum clique of an undirected graph with six vertices. We constructed data library containing n sublibraries, each sublibrary corresponded to a vertex in the given graph. Then, probe library according to the induced subgraph was designed in order to search and generate all maximal cliques. Subsequently, we performed probe operation, and all maximal cliques were generated in parallel. The advantages of the proposed model lie in two aspects. On one hand, solution to NP-complete problem is generated in just one step of probe operation rather than found in vast solution space. On the other hand, the proposed model is highly parallel. The work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP-complete problem.