Intelligent Search Method Research Articles

Image automatic-recognition scheme for dice game using structure features technique

In dice recognition, there are several situations that may cause dice image identification problems, including the color of background being the same as the dice, the shape of the dice being round, or the dice touching each other. Especially when the camera zooms in or out, the pip sizes become irregular. We analyze the dice structure features to develop an intelligent memorized least-distance search method (IMLDSM) that can achieve accurate dice detection. Compared to other schemes, our method has two major advantages: (i) Unlike other schemes using complicated classification methods to segment the dice, we adopt a novel, simple, and effective method to analyze dice structure features to achieve more accurate recognition results and (ii) the IMLDSM solves the problems of image zooming in and out, which other methods cannot. After over 250 test images that include different shapes, styles, sizes, and colors used in simulations, this scheme is proven to achieve 100% dice image identification.

A pattern recognition based intelligent search method and two assignment problem case studies

Numerous papers based on various search methods across a wide variety of applications have appeared in the literature over recent years. Most of these methods apply the following same approach to address the problems at hand: at each iteration of the search, they first apply their search methods to generate new solutions, then they calculate the objective values (or costs) by taking some constraints into account, and finally they use some strategies to determine the acceptance or rejection of these solutions based upon the calculated objective values. However, the premise of this paper is that calculating the exact objective value of every resulting solution is not a must, particularly for highly constrained problems where such a calculation is costly and the feasible regions are small and disconnected. Furthermore, we believe that for newly-generated solutions, evaluating the quality purely by their objective values is sometimes not the most efficient approach. In many combinatorial problems, there are poor-cost solutions where possibly just one component is misplaced and all others work well. Although these poor-cost solutions can be the intermediate states towards the search of a high quality solution, any cost-oriented criteria for solution acceptance would deem them as inferior and consequently probably suggest a rejection. To address the above issues, we propose a pattern recognition-based framework with the target of designing more intelligent and more flexible search systems. The role of pattern recognition is to classify the quality of resulting solutions, based on the solution structure rather than the solution cost. Hence, the general contributions of this work are in the line of "insights" and recommendations. Two real-world cases of the assignment problem, i.e. the hospital personnel scheduling and educational timetabling, are used as the case studies. For each case, we apply neural networks as the tool for pattern recognition. In addition, we present our theoretical and experimental results in terms of runtime speedup.

Loss Minimization Using Optimal Power Flow Based on Swarm Intelligences

This paper describes optimal power flow based on swarm intelligences in which the power transmission loss function is used as the problem objective. Although most of optimal power flow problems involve the total production cost of the entire power system, in some cases some different objective may be chosen. In this paper, to minimize the overall power transmission losses, four decision variables are participated. They are i) power generated from generating plants, ii) specified voltage magnitude at control substations, iii) tap position of tap changing transformers and iv) reactive power injection from reactive power compensators. Swarm intelligences are wellknown and widely accepted as potential intelligent search methods for solving such a problem. In this paper, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Differential Evolution (DE) are employed to solve optimal power flow problems. A 6-bus and 30-bus IEEE power systems are used for test. As a result, all swarm intelligences search algorithms can solve optimal power flow problems efficiently. The artificial bee colony and the differential evolution provide results better than other swarm intelligent techniques.

Pso-Based Real-Time Scheduling For Elevator Group Supervisory Control System

Abstract Elevator group supervisory control system (EGSCS) is an important and essential component in an industrial elevator system. This paper will introduce particle swarm optimization (PSO) as a heuristic intelligent search method to optimize in real time elevator traffic scheduling solution of EGSCS. The proposed PSO-based scheduling approach will be applied to effectively handle the peak elevator traffic. The overall experimental results will be used to validate its scheduling efficiency and effectiveness.

Simulation embedded artificial intelligence search method for supplier trading portfolio decision

An electric power supplier in the deregulated environment needs to allocate its generation capacities to participate in contract and spot markets. Different trading portfolios will provide suppliers with different future revenue streams of various distributions. The classical mean–variance (MV) method is inappropriate to deal with the trading portfolios whose return distribution is non-normal. In order to consider the non-normal characteristics in electricity trading, this study proposes a new model based on expected utility theory (EUT) and employs a hybrid genetic algorithm (GA) – Monte-Carlo simulation technique as solution approach. In the real market data-based numerical studies, the performances of the proposed method and the standard MV method are compared. It was found that the proposed method is able to obtain better portfolios than MV method when non-normal asset exists for trading. The simulation results also reveal the accumulation effect along trading period, which will improve the normality of the supplier trading portfolios. The authors believe the proposed method is a useful complement for the MV method and conditional value at risk (CVaR)-based methods in the supplier trading portfolio decision and evaluation.

Leading Change: The Academy of Architecture for Health Foundation Grants

On behalf of the Academy of Architecture for Health Foundation, I applaud the publication of this inaugural issue of the Health Environments Research & Design Journal (HERD).The Need-and HERD's Approach To Meeting That NeedAs the profession of architecture evolves and responds to the changing needs of clients and society, it is essential that a body of evidence be created and disseminated leading toward greater understanding of the likely outcomes of decisions on complex issues.Our profession has long valued the creative, intuitive skills of our members, but we are faced with the reality that hard evidence of favorable outcomes is essential in order to ensure the success of most efforts. For those of us in healthcare planning and design- the one-on-one relationship of the artistic genius architect and the visionary benefactor client are nonexistent-the reality is one of collaborative consultant teams and multilayered client and regulatory organizations.Each of thousands of decisions faces the scrutiny of some participant saying, Prove it. Proving the value of a decision can become a costly, time-consuming effort to the detriment of all those involved in a given project. The answer: the development of hard research that moves beyond supposition and anecdote to rigorously researched, peer-reviewed, well-documented outcomes. HERD gives the profession that opportunity.The Academy of Architecture for Health FoundationThe Academy of Architecture for Health Foundation, a separate 501(c)(3) nonprofit corporation, was established in 2000 to enhance the knowledge and effectiveness of those who create healthcare environments by identifying critical needs, funding efforts to meet those needs, and evaluating the outcomes of those efforts.Since that time the foundation has established an endowment fund and annual grants process that encourages proposals for the very research on which HERD is focused. Recent grants have included:The Academy of Neuroscience for Architecture for development of a comprehensive database.Phase I of the development of an Interactive Knowledge Database has reviewed the status of repositories of relevant to human response to architecture. Many databases were assessed; however, a single comprehensive source of that intersects the scope of architecture and healthcare design was not identified. Instead, each discipline has created separate databases with different terminologies reflecting the conceptual processes of its users.Recent developments in informatics, the science of knowledge and management, were explored as a means to link knowledge sources. The information age now provides a burgeoning wealth of knowledge. Computer systems that provide intelligent search methods linking architecture and data will better support informed design choices. Enhanced productivity in the data-gathering phase will enable the use of a broader evidence base to be more readily accessed and incorporated into architectural practice.Three models are suggested that use existing computer-based knowledge management systems.1. Architectural Web-based Interface that links existing databases across disciplines to provide text-based extraction and analysis, speeding the process of gathering.2. Graphic Architectural Interface to link specific architectural features with outcomes.3. Data-Driven Imaging simulation to link virtual design options to repositories of knowledge.Information systems that extract detailed data about the influence of architectural elements on human responses will assist manufacturers and designers in the production and use of materials and systems that better serve user needs and outcomes. Text recognition systems could search and relate in terms of relevance to an architectural project, so that programming and design reflect this early in the process. …

Solving the combinatorial double auction problem

Abstract This paper studies the solution of several types of combinatorial (double) auctions. Such auctions have recently been used in business-to-business trading in a centralized marketplace, or in multi-agent coordination systems in artificial intelligence. When the goods are indivisible, solving the winner determination problem (WDP) is an integer programming problem and NP-hard in its general form. We show that a general combinatorial double auction can be reduced to a combinatorial single-sided auction, which is a multi-dimensional knapsack problem. Next, we compare the performance of several solution approaches for this type of problem. We contrast the branch-and-bound method with the intelligent search method proposed separately in three well-referenced papers. We found that theoretically the LP relaxation bounds always dominate the bounds used in the specialized intelligent searches. We also found empirically that the performance of this branch-and-bound method is superior to the specialized search methods that have been proposed for this class of problems.

Hybrid Genetic Algorithm for the Generalised Assignment Problem

Logistics control and management typically involve multiple decision makers at different levels. They may include strategic, tactical, scheduling and operational decisions. In such a context, solving logistics optimization problems has always been a challenging task, especially in the presence of nonlinear, implicit or disjunctive constraints. However, constraints are used in optimization based on intelligent search methods. These methods usually search for an acceptable near optimal solution (or a set of solutions) rather then the optimal solution. We will explore these to identify flexibility and efficiency of optimisation afforded by metaheuristic methods.

Online design reuse tool for the support of the generation, embodiment and detailed design of products

Design reuse is becoming a widely accepted strategy to meet the increasingly fierce competition and highly diversified customer needs in product development. In this research, an integrated and systematic framework to computerize the design reuse process was formulated and a prototype online design reuse system was implemented. This system embodies the evolutionary process of design reuse and supports design reuse from the original requirements through the concept and embodiment stage to achieve a final detailed design. The prototype system retrieves products using multi-indices and a stepwise winnowing method. The advanced-search module of the system solves a design problem using two intelligent search methods to retrieve reusable products. Finally, the system can adapt these retrieved products to generate a new design. The design reuse framework is based on related design methodologies such as function taxonomy, product family, etc. Genetic algorithms and case-based reasoning are adopted in this system to achieve an intelligent search. The variant design method is used in detailed design reuse.

A multi-level hybrid framework applied to the general flow-shop scheduling problem

Despite the large amount of research conducted in flow-shop scheduling most of it has concentrated on the permutation problem in which passing is not allowed, i.e. a job cannot pass (overtake) another job while waiting in a queue to be processed by a machine. In this work the general flow-shop problem, in which passing is allowed, is dealt with as it is considered to be a better representation of flow-shop instances. The evolutionary techniques of scatter search (SS) and its generalised form, path relinking (PR) are applied to this problem as they are able to provide a wide exploration of the search space and they can be integrated with intelligent search methods such as tabu search. The SS and PR strategies are embedded within a core and shell framework. Initiated from a powerful starting solution, the core and shells iteratively search the solution space to find the best possible solutions. The core consists of a highly constrained neighbourhood, estimation strategies and a dynamic tabu tenure which provide efficiency and effectiveness during various improving and dis-improving phases of the search. Several shell strategies are superimposed on to the core in order to provide the necessary mixture of intensification and diversification. This framework is able to provide substantially better results than the tabu search approach of Nowicki and Smutnicki (Management Science, 42 (6) (1996b) 797–813). The proposed framework is able to achieve an average deviation from optimum of 8.475% while equalling 53 best solutions and finding 42 new best solutions on a suite of 202 benchmark problems. Scope and purpose This paper explores the efficient and effective interaction of intensifying and diversifying strategies in search techniques within the context of the general flow-shop-scheduling problem. In this work we aimed to create a multi-level hybrid system that is able to provide a better solution to the flow-shop-scheduling problem than the existing methods. The techniques of scatter search and path relinking along with tabu search and evolutionary algorithms provided a unifying environment for us to find new solutions. We have also demonstrated that by providing an intelligent search of the solution space some of the current barriers could be overcome.

Games solved: Now and in the future

In this article we present an overview on the state of the art in games solved in the domain of two-person zero-sum games with perfect information. The results are summarized and some predictions for the near future are given. The aim of the article is to determine which game characteristics are predominant when the solution of a game is the main target. First, it is concluded that decision complexity is more important than state-space complexity as a determining factor. Second, we conclude that there is a trade-off between knowledge-based methods and brute-force methods. It is shown that knowledge-based methods are more appropriate for solving games with a low decision complexity, while brute-force methods are more appropriate for solving games with a low state-space complexity. Third, we found that there is a clear correlation between the first-player's initiative and the necessary effort to solve a game. In particular, threat-space-based search methods are sometimes able to exploit the initiative to prove a win. Finally, the most important results of the research involved, the development of new intelligent search methods, are described.

Artificial intelligence search methods for multi-machine two-stage scheduling with due date penalty, inventory, and machining costs

This paper evaluates artificial intelligence search methods for multi-machine two-stage scheduling problems with due date penalty, inventory, and machining costs. We compare four search methods: tabu search, simulated annealing, genetic algorithm, and neighborhood search. Computational results show that the tabu search performs best in terms of solution quality. The tabu search also requires much less computational time than the genetic algorithm and simulated annealing. As expected, the neighborhood search needs the smallest computational time, but gives the worst solution quality. To further improve the solution quality and computational time, this paper proposes a two-phase tabu search. The two-phase tabu search sequentially addresses two aspects of sequencing for the same problem, order- and component-based sequencing. The order-based tabu search identifies a sequence for customers’ orders. Starting from the sequence identified for customers’ orders, the component-based tabu search fine-tunes the sequence for components produced at the fabrication stage. The results show that the two-phase tabu search is better in solution quality and computational time than the one-phase tabu search. The difference in solution quality is more pronounced at the early stage of the search. Scope and purpose Most manufacturing firms have some form of separate fabrication and assembly stages. Raw materials are transformed into components at the fabrication stage and the components are then assembled into finished products at the assembly stage. The components and assembly items are typically routed in batch quantities through several machines/work centers in a predetermined order before the finished products are delivered to customers. In this study, we model fabrication and assembly work centers as multi-machine two-stage manufacturing systems where a given machine is used to assemble/produce at least one component/product. The scheduling problem considered in this study involves a scheduling decision that achieves three objectives concurrently: (1) meeting customers’ due dates, (2) minimizing inventory cost, and (3) minimizing machining cost. Each order is an indivisible scheduling element that needs to be delivered to customers on the due date. Each order triggers successive production events from upstream to downstream according to the bill-of-material structure between components and end products. The objective of this paper are three-fold: (1) to present a solution representation for the multi-machine two-stage scheduling problem, (2) to identify the best artificial intelligence search method for this problem based on extensive computational experiments, and (3) to propose a modified tabu search method to further improve the solution quality and computational time.

A novel short-term generation scheduling technique of thermal units using ant colony search algorithms

This paper presents a novel co-operative agents approach, ant colony search algorithm (ACSA)-based scheme, for solving a short-term generation scheduling problem of thermal power systems. The main purpose of this paper is to investigate the applicability of an alternative intelligent search method in power system optimisation, particularly in short-term generation scheduling problems. The ACSA is derived from the theoretical biology of the topic of ant trail formation and foraging methods. A set of co-operating agents, ants, co-operate to find a good solution for the short-term generation scheduling problem of thermal units. In the ACSA, the state transition rule, global and local updating rules are also introduced to ensure the optimal solution. Once all the ants have completed their tours, a global pheromone-updating rule is then applied and the process is iterated until the stop condition is satisfied. The effectiveness of the proposed scheme has been demonstrated on the daily generation scheduling problem of model power systems.

Efficient and Equitable Impact Fees for Urban Water Systems

An efficient and equitable method for determination of impact fees for urban water systems is presented. A fair allocation of costs is based on n-person cooperative game theory designed to allocate costs by zones, user classes, and/or demand types. The least-cost solution of each alternative is found using an intelligent search method reported elsewhere. The proposed method is demonstrated on a small water system, and the results are compared with other cost allocation methods.

An intelligent search method for project scheduling problems

We develop an approach for implementing a real time admissible heuristic search algorithm for solving project scheduling problems with resource constraints. This algorithm is characterized by the complete heuristic learning process: state selection, heuristic learning, and search path review. The implementation approach is based on the network structure and the activity status of a project; which consists of definition of states, state transition operator, heuristic estimation, and state transition cost. The performance analysis with a benchmark problem shows that, the accumulation of heuristic learning during the search process leads to the re-scheduling of more promising activities, and finds an optimal schedule efficiently.

THE TYPICALITY OF PHASE TRANSITIONS IN SEARCH

Search is fundamental to artificial intelligence (AI) and numerous sophisticated search methods have been developed. We present a general, simple model of search processes and use it to analytically determine some typical behavior when applied to large problems. In particular, this identifies abrupt changes in overall search cost as small improvements are made in the underlying method. We also examine the robustness of this model's predictions in a range of more realistic cases. More generally, we introduce a criterion for determining when average case results reflect typical behavior which allows the method developed here to be used for investigating other large‐scale behaviors of complex AI systems.

An intelligent search method for query optimization by semijoins

The problem of finding an optimal semijoin sequence that fully reduces a given tree query is discussed. A method is presented that intelligently navigates the space of all semijoin sequences and returns an optimal solution. Experiments are reported that show that this method performs very efficiently: on average, less than 5% of the search space is searched before an optimal solution is found. Other advantages of the method are ease of implementation, generality of the cost mode considered, and ability to handle tree queries with arbitrary target lists. >