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A novel quantum evolutionary algorithm based on immune operator (MQEA) is proposed. The algorithm can find out optimal solution by the mechanism in which antibody can be clone selected, immune cell can accomplish cross-mutation and Self-adaptive mutation, memory cells can be produced and similar antibodies can be suppressed. It not only can maintain quite nicely the population diversity than the classical...
In this paper, we introduce fuzzy-neural networks– Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPN) whose fuzzy rules include the information granules (about the real system) obtained through Information Granulation. We have developed a design methodology (genetic optimization using Genetic Algorithms) to find the optimal structure for fuzzy-neural networks that...
This paper describes metaheuristic algorithm based on simulated annealing method,which is a nature-inspired method, to analyze and improve the efficiency of the design of Global Positioning System (GPS) surveying networks. Within the context of satellite surveying, a positioning network can be defined as a set of points which are coordinated by placing receivers on these point to determine sessions...
In high bit rate optical fiber communication systems, Polarization mode dispersion (PMD) is one of the main factors to signal distortion and needs to be compensated. PMD monitoring system is the key integral part of an adaptive PMD compensator. The degree of polarization (DOP) ellipsoid obtained by using a polarization scrambler can be used as a feedback signal for automatic PMD compensation. Generally,...
In this paper, we propose a novel fast evolutionary algorithm — cycle-wise genetic algorithm (CWGA) based on the theoretical analyses of a drug scheduling mathematical model for cancer chemotherapy. CWGA is more efficient than other existing algorithms to solve the drug scheduling optimization problem. Moreover, its simulation results match well with the clinical treatment experience, and can provide...
We introduce an optimization of information granules (IG)-based fuzzy model with the aid of genetic algorithms (GAs) to describe projects in terms of complexity and development time in experimental software datasets. The proposed fuzzy model implements system structure and parameter identification with the aid of IG and GAs. To identify the structure and the parameters of fuzzy model we use genetic...
Gabor Wavelet Networks (GWN) is a method for face recognition. Evolutionary Algorithms are proved to be efficient to deal with GWN optimization problems. Inver-over Evolutionary Algorithm (IOEA) mixed with GWN performs well in face recognition because of its considerable effects on complex functions optimizations, which is called IOEA-GWN put forward by us, and shows higher recognition rate than Simple...
The technology of electronic design automation (EDA) has improved the efficiency of design process, however, designer is still required much special knowledge of circuit. During the past decade, using genetic algorithm (GA) to design circuit had attracted many experts and scholars. However, too much more attention was focus on a circuit’s function and many other factors had been neglected which caused...
In this paper, we make an investigation into the discontinuous parameter identification in the case of elliptic problem. The discontinuous parameter is identified by evolutionary algorithm for the first time. For this kind of problem, we present a two-level evolutionary algorithm. The first level is the evolution for discontinuous point and the second level is the evolution for parameter. The numerical...
In large-scale, complicated Wireless Sensor Networks, the cooperation among sensor nodes is a key topic, and has been receiving more and more attention. The dynamic coalition mechanism in Multi-Agent System is an important method for this topic, and then an energy-efficient coalition formation algorithm is needed since the energy resource of WSN is restricted. This paper proposes a WSN coalition formation...
The dynamic vehicle routing problem is one of the most challenging combinatorial optimization tasks. The interest in this problem is motivated by its practical relevance as well as by its considerable difficulty. We present an approach to search for best routes in dynamic network. We propose a dynamic route evaluation model for modeling the responses of vehicles to changing traffic information, a...
Recently atomic cluster structures have been intensively studied because of their importance in physics, chemistry and material science. However, finding the lowest energy structure, which is the most stable configuration, is NP-hard. Differential Evolution (DE) algorithm is a new heuristic approach which mainly has three advantages: finding the true global minimum regardless of the initial parameter...
A special nonlinear bilevel programming problem (BLPP), whose follower-level problem is a convex programming with a linear objective function in y, is transformed into an equivalent single-level programming by using Karush-Kuhn-Tucker (K-K-T) conditions. To solve the equivalent problem effectively, a new genetic algorithm is proposed. First, a linear programming (LP) is constructed to decrease the...
The aim of the Political Districting Problem is to partition a zone into electoral districts with constraints such as contiguity, population equality, etc. By using statistical physics methods, the problem can be mapped onto a q-state Potts model system, and the political constraints are written as an energy function with interactions between sites or external fields acting on the system. This problem...
Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality...
The evaluation and management of sensor uncertainty is particularly necessary in a noisy multi-sensor context. In this paper, focusing on the potential of distributed coordination among sensor nodes based on the built-in association between wireless sensor networks and multi-agent systems, meanwhile in a rough set technique senseof uncertainty, we show how an adaptive distributed coordination framework...
A heuristic particle swarm optimization (HPSO) is proposed as a solution to one-dimensional cutting stock problem (1D-CSP), which incorporate genetic operators into particle swarm optimization (PSO). In this paper, a heuristic strategy that is based on the results of analysis of the optimal cutting pattern of particles with successful search processes is described, which process a global optimization...
It is recognized that evolutionary algorithms are inspired from evolutionary biology. In this paper, we set up a thermodynamic model of evolutionary algorithm. This model is intuitive and has a solid foundation in thermodynamics. It is our first step towards a unified theory of evolutionary algorithms.
Through analyzing schema theorem and building blocks theory, propose parallel genetic algorithms based on building blocks migration. Relying on convergence condition, receive building blocks from other populations. Using simulated annealing method prevent the density of good schema to increase greatly which will result in premature convergence. Theory analysis and experimental results show that the...
In this paper, the particle swarm optimization (PSO) algorithm with constriction factor approach (CFA) is proposed to optimal hydro generators governor Proportional-Integral-Derivative (PID) gains for small hydraulic transients. And four different integral performance criteria of turbine speed deviation such as integrated absolute error (IAE), integral of time weighted absolute value of error (ITAE),...
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