The Dynamic Matrix Control (DMC) Algorithm is a control method widely applied to industrial processes. evolutionary computation (EP) is a vibrant area of investigation, with some of the least widely known approaches b...
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ISBN:
(纸本)9783319196381;9783319196374
The Dynamic Matrix Control (DMC) Algorithm is a control method widely applied to industrial processes. evolutionary computation (EP) is a vibrant area of investigation, with some of the least widely known approaches being Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) all of which can be used in optimisation problem. This work make a comparative study of the effectiveness of the three methods to optimize the tuning parameters of the Dynamic Matrix Controller for SISO (single-input single-output) and MIMO (multi-input multi-output) linear dynamical systems with constraints.
A novel fuzzy-neural tree (FNT) is presented. Each tree node uses a Gaussian as a fuzzy membership function, so that the approach uniquely is in align with both the probabilistic and possibilistic interpretations of f...
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ISBN:
(纸本)9781479974924
A novel fuzzy-neural tree (FNT) is presented. Each tree node uses a Gaussian as a fuzzy membership function, so that the approach uniquely is in align with both the probabilistic and possibilistic interpretations of fuzzy membership. It provides a type of logical operation by fuzzy logic (FL) in a neural structure in the form of rule-chaining, yielding a novel concept of weighted fuzzy logical AND and OR operation. The tree can be supplemented both by expert knowledge, as well as data set provisions for model formation. The FNT is described in detail pointing out its various potential utilizations demanding complex modeling and multi-objective optimization therein. One of such demands concerns cognitive computing for design cognition. This is exemplified and its effectiveness is demonstrated by computer experiments in the realm of Architectural design.
Adaptive Teaching Learning Sequence requires the organization and sequencing of content in a way that conveys its structure to learner. It should be learner-centered more flexible and helps students achieve a standard...
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ISBN:
(纸本)9781509007783
Adaptive Teaching Learning Sequence requires the organization and sequencing of content in a way that conveys its structure to learner. It should be learner-centered more flexible and helps students achieve a standard of performance. In this paper, two issues are addressed: curriculum design and curriculum sequencing. The first aims to design a curriculum based on instructional design strategies and second one intents to generate 'on the spot' a learning sequence based on an adapted version of Harmony Search evolutionary computation.
In recent years, the various controller design schemes have been examined for nonlinear mechanical systems with uncertainties. As one Of Such mechanical systems, acrobots attract attention in last decade. which belong...
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ISBN:
(纸本)0889864365
In recent years, the various controller design schemes have been examined for nonlinear mechanical systems with uncertainties. As one Of Such mechanical systems, acrobots attract attention in last decade. which belong to nonholonomic systems. Several controller design schemes have been proposed for the acrobots. In this paper, a new approach to control the acrobot is considered. in which a suitable swing-up pattern is created using the real-coded genetic algorithm(GA). Then, the energy and the extra motion which are totally consumed in the swing-up motion, are evaluated on the fitness function. The effectiveness of the newly proposed scheme is numerically verified on a simulation result.
The sparse synthesis of the concentric circular antenna array (CCAA) is a very important technology because it is able to reduce the cost of the antenna array. In this paper, we first formulate a multi-objective optim...
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ISBN:
(纸本)9781538663585
The sparse synthesis of the concentric circular antenna array (CCAA) is a very important technology because it is able to reduce the cost of the antenna array. In this paper, we first formulate a multi-objective optimization problem to jointly reduce the maximum sidelobe level (SLL) and the number of the switched-on elements of the CCAA. Then, we propose a novel enhanced non-dominated sorting genetic algorithm-II (ENSGAII) to solve this problem. ENSGA-II introduces a hierarchy mechanism to improve the population utilization of the conventional non-dominated sorting genetic algorithm, thereby enhancing the accuracy and the convergence rate of the algorithm. Simulation results show that ENSGA-II obtains a lower maximum SLL with the similar number the switched-off elements compared with other algorithms Moreover, ENSGA-II has a faster convergence rate.
Aiming at the characteristics of multi-UCAV cooperative attack task assignment,a UCAVs Cooperative Task allocation algorithm based on immune evolutionary computation is *** immune mechanisms in order to maintain the d...
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ISBN:
(纸本)9781538631089;9781538631072
Aiming at the characteristics of multi-UCAV cooperative attack task assignment,a UCAVs Cooperative Task allocation algorithm based on immune evolutionary computation is *** immune mechanisms in order to maintain the diversity of population,protect outstanding individuals into the next generation of evolution,Overcome stagnancy premature phenomenon,thus improving the search ability of the *** simulation results show that the algorithm is reasonable and effective.
This paper presents evolutionary computation (EC) techniques and discusses their applicability to the optimal power flow (OPF) problem. The power flow problem is optimized to find the minimum fuel cost of all generati...
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ISBN:
(纸本)9781457702556
This paper presents evolutionary computation (EC) techniques and discusses their applicability to the optimal power flow (OPF) problem. The power flow problem is optimized to find the minimum fuel cost of all generating units while maintaining an acceptable system performance in terms of limits on the power outputs of generators, bus voltage and line flow. Different EC techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) are applied to solve the OPF problem for IEEE 30-bus system. The results are compared with the OPF solution obtained from MATPOWER that employs sequential quadratic programming to prove the effectiveness of the EC techniques. The computational results show that EC techniques work effectively and applicable to the OPF problem.
The team formation problem (TFP) concerns the process of bringing the experts together from Social Networks (SN) as teams in a collaborative working environment for a productive outcome. It was proven to be NP-hard pr...
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ISBN:
(纸本)9783030183059;9783030183042
The team formation problem (TFP) concerns the process of bringing the experts together from Social Networks (SN) as teams in a collaborative working environment for a productive outcome. It was proven to be NP-hard problem. Our findings on a static SN using evolutionary computations (EC) achieved a significant improvement than State-of-Art methods on different datasets such as DBLP and Palliative care network. Since complexity and dynamics are challenging properties of real-world SN, our current research focuses on these properties in discovering new individuals for the teams. The process of detecting suitable members for teams is typically a real-time application of link prediction. Although different methods have been proposed to enhance the performance of link prediction, these methods need significant improvement in accuracy. Moreover, we examine the changes in attributes over time between individuals of the SN, especially on the co-authorship network. We introduce a time-varying score function, to evaluate the active researcher, that uses the number of new collaborations and number of frequent collaborations with existing connections. Moreover, we incorporate the shortest distance between any two individuals and introduces a score function to evaluate the skill similarity between any two individuals to form an effective team. We introduce Link prediction as a multi-objective optimization problem for optimizing three objectives, score of active researchers, skill similarity and shortest distance. We solved this problem by applying the NSGA-II and MOCA frameworks.
In this paper we describe an approach for optimizing the parameters of a Support Vector Machine (SVM) as part of an algorithm used to detect buried objects in forward looking infrared (FLIR) imagery captured by a came...
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ISBN:
(纸本)9780819495006
In this paper we describe an approach for optimizing the parameters of a Support Vector Machine (SVM) as part of an algorithm used to detect buried objects in forward looking infrared (FLIR) imagery captured by a camera installed on a moving vehicle. The overall algorithm consists of a spot-finding procedure (to look for potential targets) followed by the extraction of several features from the neighborhood of each spot. The features include local binary pattern (LBP) and histogram of oriented gradients (HOG) as these are good at detecting texture classes. Finally, we project and sum each hit into UTM space along with its confidence value (obtained from the SVM), producing a confidence map for ROC analysis. In this work, we use an evolutionary computation Algorithm (ECA) to optimize various parameters involved in the system, such as the combination of features used, parameters on the Canny edge detector, the SVM kernel, and various HOG and LBP parameters. To validate our approach, we compare results obtained from an SVM using parameters obtained through our ECA technique with those previously selected by hand through several iterations of "guess and check".
Development of artificial intelligence has enabled automation of diverse complicated tasks, such as planning, scheduling, and natural language processing. As one of the complicated tasks, music composition involves va...
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ISBN:
(纸本)9781728169262
Development of artificial intelligence has enabled automation of diverse complicated tasks, such as planning, scheduling, and natural language processing. As one of the complicated tasks, music composition involves various aspects, e.g., melody, rhythm, harmonization, phrasing, and forms. evolutionary composition systems ordinarily formulate music composition as an optimization problem and adopt evolutionary algorithms to deal with it. This study focuses on evolutionary composition on a special genre Bossa Nova. The proposed system uses integer-coded genetic algorithm to generate the melody, followed by a postprocess for arranging suitable accompaniment. In particular, the genetic algorithm features evaluation rules derived from music theory for consonant melodies. The postprocess creates a treble part and a bass part which together form the chord accompaniment on the basis of Bossa Nova harmony and rhythmic patterns. This study conducts experiments for quality verification of the generated music. Experimental results show that the proposed rules are able to guide genetic algorithm for composing harmonious melodies, and the accompaniments from postprocess blend well with the generated melodies. By and large, the integration of generated melodies and accompaniments presents satisfactory Bossa Nova music.
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