Genetic algorithm for non-linear mixed integer programming problems and its applications
Computers and Industrial Engineering
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Penalty guided PSO for reliability design problems
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
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The aim of this study is to solve the optimization problems of different engineering designs by using nonlinear mixed integer programming mode. In the past, this type of engineering design optimization problem has been widely studied and discussed. They are usually solved through mathematical programming method or heuristics. However, there are more constraints and more constraints that cannot be satisfied. In solving this type of problems, we used a penalty guided cooperative particle swarm optimization to avoid the disadvantage of decreased efficiency from the increase of search spatial dimension and to raise the efficiency. In resolving some engineering design problems, the results shows that the solutions found by cooperative particle swarm optimization are equal or better than the best-known solutions from past literature. Thus, the results of this study indicate that cooperative particle swarm optimization is another effective method to find solutions to optimization problems.