Changshou Deng

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 biogeography-based optimization algorithm for the knapsack problem

Mathematical models of biogeography inspired the development of the biogeography-based optimization algorithm. In this article we propose a binary version of biogeography-based optimization (BBO) for the Knapsack Problem. Two new mutation operators are proposed to extend the biogeography-based optimization algorithm to binary optimization problems. We also demonstrate the performance of the resulting new binary Biogeography-based optimization algorithm in solving four Knapsack problems and compare it with that of the standard Genetic Algorithm. The simulation results show that our new method is effective and efficient for the Knapsack problem.

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.

Differential Evolution with Novel Local Search Operation for Large Scale Optimization Problems

Many real-world optimization problems have a large number of decision variables. In order to enhance the ability of DE for these problems, a novel local search operation was proposed. This operation combines orthogonal crossover and opposition-based learning strategy. During the evolution of DE, one individual was randomly chosen to undergo this operation. Thus it does not need much computing time, but can improve the search ability of DE. The performance of the proposed method is compared with two other competitive algorithms with benchmark problems. The compared results show the new methods effectiveness and efficiency.

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.