In recent decades, multi-objective evolutionary algorithms (MOEAs) are developed as powerful tools to solve multi-objective optimization problems. While the diversity of Pareto front (PF) plays an important role in th...
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In recent decades, multi-objective evolutionary algorithms (MOEAs) are developed as powerful tools to solve multi-objective optimization problems. While the diversity of Pareto front (PF) plays an important role in the performance evaluation of MOEAs, various diversity preservation strategies (DPS) have been developed. In this paper, a novel approach that inspired from the crowding distance technique is proposed to maintain the diversity of solutions in multi-objective problems (MOPs) with quite different spans of value range. In order to improve its performance, this approach is applied in a well-know MOEA NSGA II by replacing its original DPS. According to 3 test MOPs, the modified NSGA II shows a better diversity and distribution in the PF compared with the original version. Furthermore, the influence of the spans of value range on the performance of original DPS in NSGA II is discussed and the robustness of the new DPS is illustrated.
This paper describes how a non-stationary multi-objective optimization model can be used for synthesis of control of mobile robot in unknown environment. The modelled problem is solved using multi-objective genetic al...
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ISBN:
(纸本)9781424442201
This paper describes how a non-stationary multi-objective optimization model can be used for synthesis of control of mobile robot in unknown environment. The modelled problem is solved using multi-objective genetic algorithm (MOGA). Results of experiments conducted in simulation environment demonstrate the application of the described approach.
In this paper an approach to robustness analysis in evolutionarymulti-objective optimization is applied to the problem of locating and sizing capacitors for reactive power compensation (VAR planning) in electric radi...
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ISBN:
(纸本)9783642010088
In this paper an approach to robustness analysis in evolutionarymulti-objective optimization is applied to the problem of locating and sizing capacitors for reactive power compensation (VAR planning) in electric radial distribution networks. The main goal of this evolutionaryalgorithm is to find a non-dominated front containing the most robust non-dominated Solutions also ensuring diversity along the front. A concept of degree of robustness is incorporated into the evolutionaryalgorithm, which intervenes in the Computation of the fitness value assigned to Solutions. Two objective functions of technical and economical nature are explicitly considered in the mathematical model: minimization of system losses and minimization of capacitor installation costs. Constraints refer to quality of service, power How, and technical requirements. It is assumed that some input data are subject to perturbations, both concerning the objective functions and the constraints coefficients.
Genetic algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inac...
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Genetic algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inaccurate. This is because the search strategy of GAs is completely probabilistic. With a random search near the optimum sets, there is a small probability to improve current solution. Another drawback of the GAS is genetic drift. The GAs search process is a black box process and no one knows that which region is being searched by the algorithm and it is possible that GAs search only a small region in the feasible space. On the other hand, GAs usually do not use the existing information about the optimality regions in past iterations. In this paper, a new method called SOM-Based multi-objective GA (SBMOGA) is proposed to improve the genetic diversity. In SBMOGA, a grid of neurons use the concept of learning rule of Self-Organizing Map (SOM) supporting by Variable Neighborhood Search (VNS) learn from genetic algorithm improving both local and global search. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm is developed to enhance the local search efficiency in the evolutionaryalgorithms (EAs). The SOM uses a multi-objective learning rule based-on Pareto dominance to train its neurons. The neurons gradually move toward better fitness areas in some trajectories in feasible space. The knowledge of optimum front in past generations is saved in form of trajectories. The final state of the neurons determines a set of new solutions that can be regarded as the probability density distribution function of the high fitness areas in the multi-objective space. The new set of solutions potentially can improve the GAs overall efficiency. In the last section of this paper, the applicability of the proposed algorithm is examined in developing optimal policies for a real world multi-objectiv
In the design of multi-objective evolutionary algorithm, the diversity maintenance is essential to access the convergence of multi-objective optimization solutions. This paper presents a new diversity maintenance stra...
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ISBN:
(纸本)9783642015120
In the design of multi-objective evolutionary algorithm, the diversity maintenance is essential to access the convergence of multi-objective optimization solutions. This paper presents a new diversity maintenance strategy based on global crowding, which is addressed for pruning non-dominated solutions as well as preserving a wide-spread distributed solution set and maintaining population diversity. Later on, inspired by the conception of entropy in information theory, the entropy metrics is defined and applied to assess the proposed strategy. Two-dimensional and multi-dimensional numerical experiment results demonstrate that the proposed strategy shows better performance in the entropy reduction and losses Of uniform distribution than traditional diversity maintenance strategies.
A multi-objective genetic algorithm for the design of biorthogonal filter banks for embedded image coding application is presented. To be effective, the filter bank would satisfy multiple requirements related to such ...
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ISBN:
(纸本)9781424456499
A multi-objective genetic algorithm for the design of biorthogonal filter banks for embedded image coding application is presented. To be effective, the filter bank would satisfy multiple requirements related to such application. Flexibility in the design is introduced by imposing Near Perfect Reconstruction (N-PR) condition instead of entire PR condition as in conventional designs. Especially for embedded coding purposes, the filter banks are designed to be near-orthogonal. This can only be made possible by minimizing the deviation from the orthogonality in the optimization process. The optimization problem is formulated as a constrained multi-objective problem and solved using a constrained Non-dominated sorting genetic algorithm (C-NSGA) by searching solutions that achieve the best compromise between the different objective criteria, these solutions are known as Pareto Optimal Solutions. Experiment results show that our designed filter banks lead to improved performances of image coding compared to those achieved by the 9/7 filter bank of JPEG2000.
The present paper proposes an approach for prioritizing the protection of a network system exposed to a terrorist attack. The approach is based on a multi-objective optimization (MO) formulation for finding Pareto opt...
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DNA computing is a new vista of computation, which is of biochemical type. Since each piece of information is encoded in biological sequences, their design is crucial for successful DNA computation. DNA sequence desig...
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DNA computing is a new vista of computation, which is of biochemical type. Since each piece of information is encoded in biological sequences, their design is crucial for successful DNA computation. DNA sequence design is involved with a number of design criteria, which is difficult to be solved by the traditional optimization methods. In this paper, the multi-objective carrier chaotic evolution algorithm (MCCEA) is introduced to solve the DNA sequence design problem. By merging the chaotic search base on power function carrier, a set of good DNA sequences are generated. Furthermore, the simulation results show the efficiency of our method.
In this study, a multi-objective optimization of an axial compressor rotor blade has been performed through genetic algorithm with total pressure and adiabatic efficiency as objective functions. The non-dominated sort...
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In this study, a multi-objective optimization of an axial compressor rotor blade has been performed through genetic algorithm with total pressure and adiabatic efficiency as objective functions. The non-dominated sorting of genetic algorithm-II has been implemented and confidence check has been performed at k-means clustered points among all the Pareto-optimal solutions. Reynolds-averaged Navier-Stokes equations are solved to obtain the objective function and flow field inside the compressor annulus. The objective functions are used to generate Pareto-optimal front. The design variables are selected from blade lean and thickness through the Bezier polynomial formulation. By this optimization, maximum efficiency and total pressure are increased by 1.76 and 0.41 per cent, respectively, when two extreme clustered points are considered as optimal designs.
Diversity maintenance is an importance part of multi-objective evolutionary algorithm. In this paper, a new variant for the NSGA-II algorithm is proposed. The basic idea is that using the crowding distance method desi...
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ISBN:
(纸本)9783540896937
Diversity maintenance is an importance part of multi-objective evolutionary algorithm. In this paper, a new variant for the NSGA-II algorithm is proposed. The basic idea is that using the crowding distance method designed by minimum spanning tree to maintain the distribution of solutions. From an extensive comparative study with NSGA-II on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in distribution, and is also rather competitive to NSGA-II concerning the convergence.
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