Differential evolution(DE)algorithms are simple and efficient evolutionary algorithms that performwell in various optimization problems.Unfortunately,they inevitably stagnate when differential evolutionary algorithms ...Differential evolution(DE)algorithms are simple and efficient evolutionary algorithms that performwell in various optimization problems.Unfortunately,they inevitably stagnate when differential evolutionary algorithms are used to solve complex problems(e.g.,real-world artificial neural network(ANN)training problems).To resolve this issue,this paper proposes a framework based on an efficient elite centroid operator.It continuously monitors the current state of the population.Once stagnation is detected,two dedicated operators,centroid-based mutation(CM)and centroid-based crossover(CX),are executed to replace the classical mutation and binomial crossover operations in DE.CM and CX are centred on the elite centroid composed of multiple elite individuals,constituting a framework consisting of elitism centroid-based operations(CMX)to improve the performance of the individuals who fall into stagnation.In CM,elite centroid provide evolutionary direction for stagnant individuals,and in CX,elite plasmoids address the limitation that stagnant individuals can only obtain limited information about the population.The CMX framework is simple enough to easily incorporate into both classically well-known DEs with constant population sizes and state-of-the-art DEs with varying populations.Numerical experiments on benchmark functions show that the proposed CMX method can significantly enhance the classical DE algorithm and its advanced variants in solving the stagnation problem and improving performance.展开更多
A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover opera...A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover operator, mutation operators and adaptive probabilities for these operators. The algorithm is tested by two generally used functions and is used in training a neural network for image recognition. Experimental results show that the algorithm is an efficient global optimization algorithm.展开更多
Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems. Various methods are proposed for solving this pr...Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems. Various methods are proposed for solving this problem. Of all the algorithms, the ge- netic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes maybe infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.展开更多
Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio （CR） system. Current research focus on the genetic algorithm （GA） to achieve parameter optimization ...Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio （CR） system. Current research focus on the genetic algorithm （GA） to achieve parameter optimization in CR, while general GA always fall into premature convergence. Thereafter, this paper proposed a linear scale transformation to the fitness of individual chromosome, which can reduce the impact of extraordinary individuals exiting in the early evolution iterations, and ensure competition between individuals in the latter evolution iterations. This paper also introduces an adaptive crossover and mutation probability algorithm into parameter adjustment, which can ensure the diversity and convergence of the population. Two applications are applied in the parameter adjustment of CR, one application prefers the bit error rate and another prefers the bandwidth. Simulation results show that the improved parameter adjustment algorithm can converge to the global optimal solution fast without falling into premature convergence.展开更多
基金funded by National Special Project Number for International Cooperation under Grant 2015DFR11050the Applied Science and Technology Research and Development Special Fund Project of Guangdong Province under Grant 2016B010126004.
文摘Differential evolution(DE)algorithms are simple and efficient evolutionary algorithms that performwell in various optimization problems.Unfortunately,they inevitably stagnate when differential evolutionary algorithms are used to solve complex problems(e.g.,real-world artificial neural network(ANN)training problems).To resolve this issue,this paper proposes a framework based on an efficient elite centroid operator.It continuously monitors the current state of the population.Once stagnation is detected,two dedicated operators,centroid-based mutation(CM)and centroid-based crossover(CX),are executed to replace the classical mutation and binomial crossover operations in DE.CM and CX are centred on the elite centroid composed of multiple elite individuals,constituting a framework consisting of elitism centroid-based operations(CMX)to improve the performance of the individuals who fall into stagnation.In CM,elite centroid provide evolutionary direction for stagnant individuals,and in CX,elite plasmoids address the limitation that stagnant individuals can only obtain limited information about the population.The CMX framework is simple enough to easily incorporate into both classically well-known DEs with constant population sizes and state-of-the-art DEs with varying populations.Numerical experiments on benchmark functions show that the proposed CMX method can significantly enhance the classical DE algorithm and its advanced variants in solving the stagnation problem and improving performance.
文摘A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover operator, mutation operators and adaptive probabilities for these operators. The algorithm is tested by two generally used functions and is used in training a neural network for image recognition. Experimental results show that the algorithm is an efficient global optimization algorithm.
基金the National Natural Science Foundation of China(Nos.60574071 and70771080)
文摘Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems. Various methods are proposed for solving this problem. Of all the algorithms, the ge- netic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes maybe infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.
基金supported by the National Natural Science Foundation of China (61172073)National Key Special Program(2012ZX03003005)+1 种基金the State Key Laboratory of Rail Traffic Control and Safety (RCS2011ZT003)Beijing Jiaotong University and the Fundamental Research Funds for the Central Universities
文摘Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio （CR） system. Current research focus on the genetic algorithm （GA） to achieve parameter optimization in CR, while general GA always fall into premature convergence. Thereafter, this paper proposed a linear scale transformation to the fitness of individual chromosome, which can reduce the impact of extraordinary individuals exiting in the early evolution iterations, and ensure competition between individuals in the latter evolution iterations. This paper also introduces an adaptive crossover and mutation probability algorithm into parameter adjustment, which can ensure the diversity and convergence of the population. Two applications are applied in the parameter adjustment of CR, one application prefers the bit error rate and another prefers the bandwidth. Simulation results show that the improved parameter adjustment algorithm can converge to the global optimal solution fast without falling into premature convergence.
