Evolving Digital Circuits Using Hybrid Particle Swarm Optimization and Differential Evolution


This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher.

DOI: 10.1142/S0129065706000585


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@article{Moore2006EvolvingDC, title={Evolving Digital Circuits Using Hybrid Particle Swarm Optimization and Differential Evolution}, author={Phillip W. Moore and Ganesh K. Venayagamoorthy}, journal={International journal of neural systems}, year={2006}, volume={16 3}, pages={163-77} }