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This paper proposes a new approach to system modeling using continuous-time recurrent fuzzy systems (CTRFS). The approach is based on the representation of CTRFS as hybrid systems. With this representation, various forms of a priori knowledge about the system to be modeled can be incorporated. This allows a reasonable reduction of optimization parameters and hence, avoids overfitting. Furthermore,...
In this paper, a multi-objective heuristic-based method of design of a fuzzy controller for an inverted pendulum-cart system is investigated. A heuristic search optimization called “Imperialist Competitive Algorithm” is proposed to be used in generating and optimizing the fuzzy rules engine. The main purpose of this paper is to design a model-free nonlinear controller for inverted pendulum-cart system...
In this paper we describe an improved version of self-adaptive differential evolution algorithm. Our algorithm uses more strategies, ageing mechanism to reinitialize an individual which stagnates in local optima, an ϵ level controlling of constraint violation. The performance of the proposed algorithm is evaluated on the set of 18 scalable benchmark functions provided for the CEC 2010 competition...
MOEA/D is a generic multiobjective evolutionary optimization algorithm. MOEA/D needs a approach to decompose a multiobjective optimization problem into a number of single objective optimization problems. The commonly-used weighted sum approach and the Tchebycheff approach may not be able to handle disparately scaled objectives. This paper suggests a new decomposition approach, called NBI-style Tchebycheff...
An efficient design of a Multi-Objective Learning Classifier System for multi-flight navigation is presented. A classifier is represented by a set of rules, which are used to simultaneously navigate all the flights in the airspace. Navigation of a flight is based on the relation of the flight with factors of the air traffic environment such as wind, storm as well as other flights. This system continually...
The optimization method for public traffic line network is studied according to the feature of line network optimization problem. By using system science theory, seven objective functions and three constraints of line network optimization are established. An ideal solution decision method based on entropy weight and multi-objective programming is proposed. The method is simple and easy to use. By...
This paper proposes a new approach for reactive power planning (RPP) or VAR Planning with two major steps. First, the fuzzy clustering algorithm is employed to select candidate locations for installing new shunt VAR sources. Second, a piecewise linear method is proposed for VAR capacity optimization via minimizing the total cost for system operation. In the cost minimization model, the tie-line total...
In this paper we present self-adaptive differential evolution algorithm jDElsgo on large scale global optimization. The experimental results obtained by our algorithm on benchmark functions provided for the CEC 2010 competition and special session on Large Scale Global Optimization are presented. The experiments were performed on 20 benchmark functions with high dimension D = 1000. Obtained results...
Over the last years, interest in hybrid meta-heuristics has risen considerably in the field of optimization. Combinations of methods such as evolutionary algorithms and local searches have provided very powerful search algorithms. However, due to their complexity, the computational time of the solution search exploration remains exorbitant when large problem instances are to be solved. Therefore,...
In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, high predictive accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts in making the elicited model as interpretable as possible, which leads to a difficult optimization...
In this paper, Differential Evolution based approach with a novel dynamic constraint-handling mechanism is proposed to solve constrained real-parameter optimization. This is implemented by restating the single-objective constrained optimization as a set of single-objective unconstrained problems and dynamically assigning to the individual adaptively as its fitness. Three selection criteria based on...
This paper presents an algorithm for multiobjective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighborhood of each agent. These heuristics are complemented with two restart mechanisms and a combination of a local and global archive. The hybrid algorithm is tested at first on a set...
The increasing number of disasters reminded us of the importance of protecting critical infrastructure networks, including the transportation networks. In this paper, we propose a system-optimized transportation network model with additional disruption hedging investment options when a transportation network link capacity is degradable. Theoretical results are provided to form a foundation for the...
A new language, Feldspar, is presented, enabling high-level and platform-independent description of digital signal processing (DSP) algorithms. Feldspar is a pure functional language embedded in Haskell. It offers a high-level dataflow style of programming, as well as a more mathematical style based on vector indices. The key to generating efficient code from such descriptions is a high-level optimization...
Gini coefficient has been widely used for evaluation the balance and fairness of a target relative to another index of the status quo, but the reports related to planning analysis are limited. In this paper, we set up balance distribution optimal model for the plan of total control of environmental pollutants, and then take a region of North China as a case to apply the supposed model indecision making...
A problem that is commonly faced by large-scale population system is the high-dimensionality of data that needs to be processed at a given time. In this paper, a new face recognition training structure is proposed in which the large-scale population is split into smaller groups to be processed separately. To improve classification the proposed system uses global and local linear discriminant analysis...
An optimization based algorithm for solving the generalized assignment problem (GAP) is proposed in the paper. The primary concept underlying our algorithm is a stepwise incomplete branch and bound method guided by the information of bases and reduced costs revealed in solving linear programming (LP) relaxations of the GAP. We repeatedly used the information to fix binary integer variables of GAP...
In this paper, an algorithm for load balancing in the parallel and distributed systems is proposed. Firstly, all jobs are assigned to the machines according to classical Min-min algorithm. Then performance of the algorithm is evaluated by computing the fairness index. If the value of the fairness index is not within the proper range, an improvement algorithm can be executed over the intermedial results...
Finding feasible solution for a nonlinear equations is a very challenging problem and generally needs a high computational efforts. In this paper, a new swarm intelligent algorithm, social cognitive optimization algorithm (SCOA), is proposed to solve this problem. In SCOA, each individual simulates one natural person. All of them are communicated through cooperation and competition to increase social...
Scaled Convex Hulls (SCHs) have been recently proposed by Liu et al. as the basis of a method to build linear classifiers that, when extended to kernel settings, provides an alternative approach to more established methods such as SVMs. Here we show how to adapt the Mitchell-Dem'yanov-Malozemov (MDM) algorithm to build such SCH-based classifiers by solving a concrete nearest point problem. We shall...
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