Bingyan Zhao

Novel Binary Differential Evolution Algorithm for Discrete Optimization

New Binary Differential Evolution algorithm was proposed for the combinatorial optimization problem. With the same framework of the original Differential Evolution algorithm, three new operators were used to expand the continuous field of the original Differential Evolution to the discrete field. Firstly, a new operator, mapping operator, in the new algorithm was used to ensure the original mutation operator still effective. Then a new S operator, with sigmoid function, was used to keep the result of the mutation operator falls in the interval. Before the crossover operator, an inverse mapping operator transformed the continuous numbers to discrete. Two initial simulation results show it is effective and useful.

Novel binary Differential Evolution without scale factor F

Differential Evolution is a competitive optimization technique over continuous space. The operation in the original Differential Evolution is simple, however, the mechanism, in the Differential Evolution makes it practically impossible to effectively use the original Differential Evolution to the binary space. A novel binary mutation operation was defined to enable the Differential Evolution to operate within the binary space. The new binary mutation works well in the binary space without the original scale factor F. Initial experimental results of three different sizes of knapsack problems and the One-Max problem indicate the effectiveness and validity of the binary Differential Evolution operating in binary space.

Hybrid-Coding Binary Differential Evolution Algorithm with Application to 0-1 Knapsack Problems

Binary Differential Evolution algorithm was proposed for discrete optimization problem. Firstly, a new operator, boundary-handling operator, was added to the original Differential Evolution to ensure each population generated by the mutation and crossover operator comply with the boundary constraints. Then a new hybrid coding Differential Evolution algorithm with mapping method was put forward to deal with the discrete optimization problem. And lastly, a new selection operator was employed to deal with constraints directly. Two initial simulation results of knapsack problem with different variables show it is effective and useful. Hybrid coding Differential Evolution algorithm is a new effective way for solving the discrete optimization problem.

Novel binary encoding differential evolution algorithm

Differential Evolution (DE) algorithm is a successful optimization method in continuous space and has been successfully applied in many different areas. The operators used in DE are simple, however, the mechanism in which the operators are defined, makes it impossible to apply the standard DE directly to the problems in binary space. A novel binary encoding DE (BDE) was proposed to extend DE for solving the optimization problems in binary space. A mixed expression, which constitutes of an arithmetical expression and a logical expression, was used to construct a new mutation operator. And then with a predefined probability, the result of the mutation operator was flipped. Initial experiment results indicate the novel BDE is useful and effective.

Binary Differential Evolution algorithm with new mutation operator

Differential Evolution (DE) algorithm is well known real parameter optimization method and has been successfully applied in many different fields. The operators used in DE are simple, however, the mechanism in which the operators are defined, makes it impossible to apply the standard DE to combinatorial problems directly. A novel binary DE was proposed to extend DE to solve binary space optimization problems. New mutation rules based on knowledge was used to guide the mutation operator. Initial experiment results indicate the effectiveness of the new mutation operator and feasibility of DE operating in binary space.

New penalty function with differential evolution for constrained optimization

The penalty function is one of the most commonly used approaches for constrained optimization problems. However, it often leads to additional parameters and the parameters are not easy for the users to select. A new way without additional parameters to deal the constrained optimizations was proposed. Firstly, a new penalty function was defined using the constrained functions without additional parameters. Secondly, combining the penalty function and the original objective function, a new objective function without any constrained conditions was got. Then differential evolution algorithm was used to solve the non-constrained optimization problem. The numerical experiments show its advantage over the other existing method.

Binary Encoding Differential Evolution with Application to Combinatorial Optimization Problem

Differential Evolution algorithm is a new competitive heuristic optimization algorithm in the continuous field. The operators in the original Differential Evolution are simple; however, these operators make it impossible to use the Differential Evolution in the binary space directly. Based on the analysis of problems led by the mutation operator of the original Differential Evolution in the binary space, a new mutation operator was proposed to enable this optimization technique can be used in binary space. The new mutation operator, which is called semi-probability mutation operator, is a combination of the original mutation operator and a new probability-based defined operator. Initial experimental results of two different combinatorial optimization problems show its effectiveness and validity.

Hybrid differential evolution for knapsack problem

A hybrid Differential Evolution algorithm with double population was proposed for 0-1 knapsack problem The two populations play different roles during the process of evolution with the floating-point population as an engine while the binary population guiding the search direction Each gene of every chromosome in the floating-point encoding population is restricted to the range [-1, 1], while each gene of every chromosome in the binary encoding population is zero or one A new mapping operation based on sign function was proposed to generate the binary population from the floating-point population And a local refining operation called discarding operation was employed in the new algorithm to fix up the solutions which are infeasible Three benchmarks of 0-1 knapsack problem with different sizes were used to verify the new algorithm and the performance of the new algorithm was also compared with that of other evolutionary algorithms The simulation results show it is an effective and efficient way for the 0-1 Knapsack problem.

Binary Encoding Differential Evolution for Combinatorial Optimization Problems

Differential Evolution algorithm is a new competitive heuristic optimization algorithm in the continuous field. The operators in the original Differential Evolution are simple, however, these operators make it impossible to use the Differential Evolution in the binary space directly. Based on the analysis of problems led by the mutation operator of the original Differential Evolution in the binary space, a new mutation operator was proposed to enable this optimization technique can be used in binary space. The new mutation operator, which is called semi-probability mutation operator, is a combination of the original mutation operator and a new probability-based defined operator. Initial experimental results of two different combinatorial optimization problems show its effectiveness and validity.

Integer Encoding Differential Evolution Algorithm for Integer Programming

A novel integer encoding Differential Evolution (IEDE) algorithm was proposed for integer optimization problems in this paper. Based on the standard framework of the traditional DE, the population was encoding with integer. The IEDE inherited the crossover operator and selection operator from the traditional DE directly. And a new integer mutation operator was defined to deal with the integer encoding individual. Several initial simulation results show it is effective and efficient in solving the integer optimization problems. The IEDE is a new effective way for the integer optimization problems.