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The method of amending chord length parameterization was used to process different feasible point vectors of ship's section area, and the parameterized cubic spline curves were used to express the different subsections of ship's section area curve (SAC). The work of designing a good ship's SAC was converted to a problem of multi-objective optimization. The objects of single functions were set as the...
Swarm-inspired optimization has become very popular in recent years. The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems effectively, with the computational burden and colonies. Particle Swarm Optimization (PSO) and Ant colony Optimization (ACO) have attracted the interest of researchers due to its simplicity, effectiveness...
We have studied the C3 photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the C3 photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model...
A hybrid multi-objective optimization algorithm based on partial aspects of evolution strategy combining stochastic and deterministic elements with the aim of a high efficiency and high scalability suitable for massively distributed finite element analysis is investigated. The selection and generation process of solution candidates depend on density in design variable and objective space. New solution...
In the paper an adapted version of the differential evolution algorithm has been created to solve a multi-objective optimization problem. Multi-objective Differential Evolution Algorithm using vector differences for perturbing the vector population with self adaptation is introduced. Through the combination of mutation strategies and self adaptation of crossover and differentiation constants the proposed...
A new fuzzy multi-population cooperative immune clone algorithm, called fmcica for the multi-objective optimization problems is proposed in this paper. We firstly make both of fuzzy rules and greedy algorithm infuse into the antibody decoding, sequentially enhancing the intelligent learning ability of antibody; And with the introducing of concepts of the vector affinity it can solve the difficult...
Dynamic topologies and constrained recourses are two main characters of satellite networks. These characters require a specialized and efficient routing algorithm. In this paper, a QoS routing algorithm based on PEC multi objective optimization method is proposed to find paths that satisfy all user QoS requirements efficiently. A new routing scheme is also developed to reduce the computing load in...
Nonlinear constrained problem has been deemed as a hard problem. This paper proposes a kind of evolutionary algorithm for constrained programming. The constrained conditions are converted into an objective and then the constrained programming is transformed into a special bi-objective unconstrained problem. The Pareto concept of multiobjective programming is introduced, then crossover operator using...
Honeybee mating optimization recently proposed is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. In this paper, inspired from virus evolutionary, by redefining mating operator and breeding operator we presented a new honeybee swarm optimization algorithm for multi-objective optimization. The test results show its performance in conducting an extensive search...
In this paper, a multi-objective PSO algorithm based on escalating strategy will be proposed. The main idea of this escalating strategy is to re-generate the whole evolutionary population with some technology, which results in a new population significantly indifferent from the old one while inheriting the evolutionary information from the history. By this way, the performance on global convergence...
A Multi-Objective Particle Swarm Algorithm(MOPSA) is applied to the optimization of Intensity Modulated Radiation Treatment inverse planning. The test cases of irregular field under the condition of water phantom prove the practicability of the algorithm. The optimization results compared with other algorithms proposed by our group show that the algorithm has clearly advantages on convergence speed.
Product design optimization under model or input variable uncertainty is commonly required, in which robustness and reliability are two important attributes of the design. In structure design, it is critical to maintain the design feasibility (or reliability); while at the same time, to counter manufacturing variations, robust design is employed in order to obtain high product quality. It is necessary,...
The multi-objective optimization problem in flexible job-shop scheduling was discussed. According to the characteristics of flexible job-shop scheduling, a new self-adaptive genetic algorithm was proposed, and the model of Multi-Objective Flexible Job-shop Scheduling (MOFJS) was set up. At last, by programming with Matlab and Visual C++, the algorithm was applied to solve the MOFJS problem in Chinese...
Association rule mining based on support and confidence generates a large number of rules. However, post analysis is required to obtain interesting rules as many of the generated rules are useless. We pose mining association rules as multi-objective optimization problem where objective functions are rule interestingness measures and use NSGA-II, a well known multi-objective evolutionary algorithm...
In this paper, a method based on multi-objective evolutionary algorithms, is described to establish a correspondence between the ancient Indus script, with its descendant, a script called Brahmi that can be easily read. Our approach, which establishes a map between one or more symbols of the Indus script with each Brahmi character, is a standard problem in combinatorial optimization. As the Indus...
A new custom evolutionary algorithm was developed and implemented to solve multiple objective multi-state reliability optimization design problems. This new algorithm uses the universal moment gene-rating function approach to evaluate the different re-liability or availability indices of the system which have various levels of performance ranging from perfectly functioning to completely failed. And...
Differential evolution (DE) is a kind of simple but powerful evolutionary optimization algorithm with many successful applications. This paper proposed a multiobjective differential evolutionary algorithm based on opposite operation. Firstly, in the initialization of the algorithm, the opposite points of randomly generated individuals are calculated in order to make the initial population better....
Resources optimization of cross-layer is a typical multi-objective optimization problem. In this paper, an adaptive clone and neighbor selection algorithm is proposed to resolve optimization resources allocation in cognitive radio networks (CRNs). The algorithm uses the adaptive cloning operator, neighborhood search operator to improve the performance of algorithm. Simulation comparisons for typical...
On account of route optimization of bus dispatching, it is proposed to use genetic ant algorithm (GAA) for solution. A mathematic model for multi-objective bus route optimization and selection under limited conditions is developed, introducing the evolutionary process of genetic variation to improve the optimization of ant colony algorithm and also the optimal decision updating and identification...
In this study, a novel clustering-based selection strategy of nondominated individuals for evolutionary multi-objective optimization is proposed. The new strategy partitions the nondominated individuals in current Pareto front adaptively into desired clusters. Then one representative individual will be selected in each cluster for pruning nondominated individuals. In order to evaluate the validity...
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