Generating optimal topologies in structural design using a homogenization method
Computer Methods in Applied Mechanics and Engineering
Evolutionary structural optimization for problems with stiffness constraints
Finite Elements in Analysis and Design
Journal of Global Optimization
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Topology optimization of structures using ant colony optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Structural topology optimization using ant colony optimization algorithm
Applied Soft Computing
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the usefulness of non-gradient approaches in topology optimization
Structural and Multidisciplinary Optimization
Comparison of evolutionary-based optimization algorithms for structural design optimization
Engineering Applications of Artificial Intelligence
Intelligent optimal design of spatial structures
Computers and Structures
Hi-index | 0.00 |
Differential evolution (DE) is an efficient population based algorithm used to solve real-valued optimization problems. It has the advantage of incorporating relatively simple and efficient mutation and crossover operators. However, the DE operator is based on floating-point representation only, and is difficult to use when solving combinatorial optimization problems. In this paper, a modified binary differential evolution (MBDE) based on a binary bit-string framework with a simple and new binary mutation mechanism is proposed. Two test functions are applied to verify the MBDE framework with the new binary mutation mechanism, and four structural topology optimization problems are used to study the performance of the proposed MBDE algorithm. The experimental studies show that the proposed MBDE algorithm is not only suitable for structural topology optimization, but also has high viability in terms of solving numerical optimization problems.