A hybrid system for planning the development level of resort

In today’s world, a resort is a popular place that provides not only relaxation and recreation but also beautiful surroundings, high quality food, even facilities to exercise and do other healthy activities. Planning the development level is one of the more important processes in a resort development project. However, planners often subjectively overestimate the project or cater to the preferences of the investors, resulting in an over-developed or imbalanced development. This paper provides a system that helps the planner to search for near-optimal amenity development level. Integrating genetic algorithms and simulation, it employs a dual-loop optimization model to propose advice to be used in the planning stage. Because the complex and dynamic analysis is done by the system instead of by the planner, it speeds up the decision-making process of the planning stage.     

A hybrid ant colony optimization for continuous domains

Research on optimization in continuous domains gains much of focus in swarm computation recently. A hybrid ant colony optimization approach which combines with the continuous population-based incremental learning and the differential evolution for continuous domains is proposed in this paper. It utilizes the ant population distribution and combines the continuous population-based incremental learning to dynamically generate the Gaussian probability density functions during evolution. To alleviate the less diversity problem in traditional population-based ant colony algorithms, differential evolution is employed to calculate Gaussian mean values for the next generation in the proposed method. Experimental results on a large set of test functions show that the new approach is promising and performs better than most of the state-of-the-art ACO algorithms do in continuous domains.     

广义分层混合模糊系统及其泛逼近性

为避免广义模糊系统出现规则爆炸现象, 引进实参数将Mamdani模糊系统和T--S模糊系统统一起来建立广义分层混合模糊系统, 进而给出了广义分层混合模糊系统的数学表示. 此外, 应用方形分片线性函数的优良性质获得该广义分层混合模糊系统在积分模意义下仍具有泛逼近性, 并通过实例及仿真说明该分层混合模糊系统能够避免模糊规则爆炸问题.     

A hybrid solution method for fuzzy train formation planning

The train formation plan (TFP) determines the routing and frequency of trains, and assigns the demands to trains. In this paper, in order to consider the real-life condition of railways, a mathematical model with fuzzy costs is proposed for train formation planning in Iranian railway. In this fuzzy model, the costs are considered in three scenarios, namely optimistic, normal and pessimistic. The model is formulated based on the fixed-charge capacitated multicommodity network design problem. Since the TFP problem is NP-hard, an efficient hybrid algorithm combining local branching and relaxation induced neighborhood search methods is presented. A three-step method is applied for parameter tuning using design of experiments approach. To evaluate the efficiency and effectiveness of the proposed algorithm, the results are compared with those of the state-of-the-art optimization software.     

A photomontage system for site planning

The note describes the photomontage system for accurately visualizing the effects of altering a landscape, either by new buildings, new roads, urban renewal or any disturbance of the environment. An example is given of the use of photomontage in a motorway construction scheme.     

A case-based system for process planning

Process planning is the phase of manufacturing that is concerned with the selection, and sequencing of manufacturing operations necessary to transform an initial stock material into a finished part. It is a tedious operation that requires highly skilled and experienced personnel of which there is currently great shortage. To disseminate the costly expertise and to reduce process planning times, which tend to add high costs to small batch production, many automated computer systems have been introduced and applied. All these systems, though, require the supervision of an experienced human being and fail to capture the knowledge and reasoning behind process planning decisions. The causes of the lack of an efficient automatic system for process planning are the unique characteristics of the domain. In contrast to other areas where knowledge-based and expert systems have been applied, process planning demands the solution of several different planning problems before a final solution is achieved. Every change introduced to the previous world model creates a completely new world model with its own, new constraints and preconditions that nedd to be satisfied. This, in turn, affects the applicability of the knowledge to the new model.

To provide solutions to the above problems we have designed a prototypical knowledge-based system that uses the high level, dynamic memory structures of MOPs (Memory Organization Packages), meta-MOPs and TOPs (Thematic Organization Packets) to rearrange its knowledge according to its experiences and to predict and avoid errors. To model the dynamic, sharable knowledge and the constantly changing world model a knowledge-representation language named TOLTEC has been designed and used in the system. Furthermore, we propose a novel planning method based on abstract plans and constraints that can solve planning problems in uncertain domains given incomplete information.     

A system for knowledge-based process planning

We describe the application of problem solving, knowledge based methods in creating process plans in manufacturing. The planner presented - called TOLTEC - is designed for experiential domains and bases its operation on the use of cases in a dynamic memory environment. We will describe the way TOLTEC creates process plans by utilizing previous experiences, dynamic clustering of its memories and dynamic constraint generation and by shifting its focus of attention to different features of the workpiece by using importance values. Also, we will present how TOLTEC learns by modifying its memories according to new experiences and how it helps bridge some of the gap between design and manufacturing by detecting design errors.The emphasis in this paper is more on the application aspects of our system and the examples presented will demonstrate the abilities of TOLTEC to design process plans, detect design errors, predict manufacturing errors, recover from planning errors, handle multiple branching solutions and improve its performance by utilizing learning techniques.     

A new hybrid algorithm for path planning of mobile robot

The Journal of Supercomputing - The selection of algorithm is the most critical part in the mobile robot path planning. At present, the commonly used algorithms for path planning are genetic...     

Complexity results for standard benchmark domains in planning

The efficiency of AI planning systems is usually evaluated empirically. For the validity of conclusions drawn from such empirical data, the problem set used for evaluation is of critical importance. In planning, this problem set usually, or at least often, consists of tasks from the various planning domains used in the first two international planning competitions, hosted at the 1998 and 2000 AIPS conferences. It is thus surprising that comparatively little is known about the properties of these benchmark domains, with the exception of Blocksworld, which has been studied extensively by several research groups.In this contribution, we try to remedy this fact by providing a map of the computational complexity of non-optimal and optimal planning for the set of domains used in the competitions. We identify a common transportation theme shared by the majority of the benchmarks and use this observation to define and analyze a general transportation problem that generalizes planning in several classical domains such as Logistics, Mystery and Gripper. We then apply the results of that analysis to the actual transportation domains from the competitions. We next examine the remaining benchmarks, which do not exhibit a strong transportation feature, namely Schedule and FreeCell.Relating the results of our analysis to empirical work on the behavior of the recently very successful FF planning system, we observe that our theoretical results coincide well with data obtained from empirical investigations.     

A hybrid evolutionary approach for solving constrained optimization problems over finite domains

A novel approach for the integration of evolution programs and constraint-solving techniques over finite domains is presented. This integration provides a problem-independent optimization strategy for large-scale constrained optimization problems over finite domains. In this approach, genetic operators are based on an arc-consistency algorithm, and chromosomes are arc-consistent portions of the search space of the problem. The paper describes the main issues arising in this integration: chromosome representation and evaluation, selection and replacement strategies, and the design of genetic operators. We also present a parallel execution model for a distributed memory architecture of the previous integration. We have adopted a global parallelization approach that preserves the properties, behavior, and fundamentals of the sequential algorithm. Linear speedup is achieved since genetic operators are coarse grained as they perform a search in a discrete space carrying out arc consistency. The implementation has been tested on a GRAY T3E multiprocessor using a complex constrained optimization problem.     

A planning system for robot construction tasks

This paper describes BUILD, a computer program which generates plans for building specified structures out of simple objects such as toy blocks. A powerful heuristic control structure enables BUILD to use a number of sophisticated construction techniques in its plans. Among these are the incorporation of pre-existing structure into the final design, pre-assembly of movable sub-structures on the table, and the use of extra blocks as temporary supports and counterweights in the course of the construction.BUILD does its planning in a modeled 3-space in which blocks of various shapes and sizes can be represented in any orientation and location. The modeling system can maintain several world models at once, and contains modules for displaying states, testing them for inter-object contact and collision, and for checking the stability of complex structures involving frictional forces.Suggestions are included for the extension of BUILD-like systems to other domains. Also discussed are the merits of BUILD's implementation language, conniver, for this type of problem solving.     

A hybrid model for a multiproduct pipeline planning and scheduling problem

Brazilian petrobras is one of the world largest oil companies. Recurrently, it faces a very difficult planning and scheduling problem: how to operate a large pipeline network in order to adequately transport oil derivatives and biofuels from refineries to local markets. In spite of being more economical and environmentally safer, the use of a complex pipeline network poses serious operational difficulties related to resource allocation and temporal constraints. The current approaches known from the literature only consider a few types of constraints and restricted topologies, hence they are far from being applicable to real instances from petrobras. We propose a hybrid framework based on a two-phase problem decomposition strategy. A novel Constraint Programming (CP) model plays a key role in modelling operational constraints that are usually overlooked in literature, but that are essential in order to guarantee viable solutions. The full strategy was implemented and produced very adequate results when tested over large real instances.     

A hybrid intelligent model for order allocation planning in make-to-order manufacturing

This paper investigated a multi-objective order allocation planning problem in make-to-order manufacturing with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization (MOMO) process, a Monte Carlo simulation technique and a heuristic pruning technique, is developed to tackle this problem. The MOMO process, combining a NSGA-II optimization process with a tabu search, is proposed to provide Pareto optimal solutions. Extensive experiments based on industrial data are conducted to validate the proposed model. Results show that (1) the proposed model can effectively solve the investigated problem by providing effective production decision-making solutions; (2) the MOMO process has better capability of seeking global optimum than an NSGA-II-based optimization process and an industrial method.     

A novel hybrid particle swarm optimization for multi-UAV cooperate path planning

Applied Intelligence - The path planning of unmanned aerial vehicle (UAV) in three-dimensional (3D) environment is an important part of the entire UAV’s autonomous control system. In the...     

A hybrid learning system for image deblurring

In this paper we propose a 3-stage hybrid learning system with unsupervised learning to cluster data in the first stage, supervised learning in the middle stage to determine network parameters and finally a decision-making stage using voting mechanism. We take this opportunity to study the role of various supervised learning systems that constitute the middle stage. Specifically, we focus on one-hidden layer neural network with sigmoidal activation function, radial basis function network with Gaussian activation function and projection pursuit learning network with Hermite polynomial as the activation function. These learning systems rank in increasing order of complexity. We train and test each system with identical data sets. Experimental results show that learning ability of a system is controlled by the shape of the activation function when other parameters remain fixed. We observe that clustering in the input space leads to better system performance. Experimental results provide compelling evidences in favor of use of the hybrid learning system and the committee machines with gating network.     

A hybrid expert system for investment advising

Abstract: Investor is an investment advising hybrid expert system which incorporates two of the most powerful techniques developed in artificial intelligence, the connectionist and the logic programming paradigms, in a unified model. Investor employs a neural network model to detect the interest rate trends and a rule-based component to classify the user into the appropriate investor model. The investor model and the interest rate information are then used by the knowledge base to infer the appropriate investment strategy. Genetic algorithms and classifier systems were employed to train and configure the architecture of Investor's neural network component.