Testing and optimization of a semi- automatic prostate boundary segmentation algorithm using virtual operators. Medical Physics 2003;30(7):1637-47.Ladak H, Wang Y, Downey D, Fenster A, Testing and Optimization of a Semi-Automatic Prostate Boundary Segmentation Algorithm using Virtual Operators. ...
closed-loop monitoring: Establish a complete closed-loop monitoring mechanism. In the R&D phase, the test coverage is improved through the design of multi-party test cases including product, testing, R&D, etc., and real-time dynamic test set monitoring and manual evaluation are used online to e...
This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization algorithm (SBOA), inspired by the survival behavi
The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its
https://ocw.mit.edu/courses/sloan-school-of-management/15-082j-network-optimization-fall-2010/assignments/ 本文档内容主要参考《NETWORK FLOWS》[3]完成,另外上述进阶内容也可参考该文献进行学习。 另外,关于本文档介绍的基础算法也可参考《Development and testing of dynamic traffic assignment and simulation ...
Many existing test sequence generation techniques uses genetic algorithm for test-case generation in software testing. The Genetic Algorithm (GA) is an optimization heuristic technique that is implemented through evolution and fitness function. It generates new test cases from the existing test sequence....
A Metaheuristic Optimization Algorithm refers to a family of methods used in computer science that aim to find a global optimum by repeatedly evaluating the objective function without relying on gradient information. These algorithms, such as evolutionary algorithms, particle swarm optimization, and simula...
Types and their restrictions are: 'doubleVector'— Use this option if the individuals in the population have type double. Also, the recommended data type for mixed integer programming is 'doubleVector', using the technique in Mixed Integer ga Optimization. 'doubleVector' is the default data ...
Optimization techniques are divided into single- and multi-objective [10]. The first type achieves the optimal solution by comparing the utilized objective function. In the case of multi-objective optimization, the goal is to find the POSs [36,37]. Solving multi-objective problems is classified ...
Differential evolution (DE) has become popular for solving optimization problems and has proven to outperform other well-known algorithms in solving continuous optimization problems with different properties [1,2,3]. It starts with an initial population and constructs a new solution from the current ...