A New Optimization Approach Based on Rotational Mutation and Crossover Operator
This is an incremental improvement for researchers and practitioners in optimization, as it enhances efficiency in solving global optimization problems.
The authors tackled the problem of finding global optima in continuous functions by proposing a new optimization method (RMCGA) that uses rotational mutation and crossover operators, achieving faster convergence with fewer generations compared to existing algorithms like DE and PGA.
Evaluating a global optimal point in many global optimization problems in large space is required to more calculations. In this paper, there is presented a new approach for the continuous functions optimization with rotational mutation and crossover operator. This proposed method (RMC) starts from the point which has best fitness value by elitism mechanism and after that rotational mutation and crossover operator are used to reach optimal point. RMC method is implemented by GA (Briefly RMCGA) and is compared with other wellknown algorithms such as: DE, PGA, Grefensstette and Eshelman[15,16] and numerical and simulating results show that RMCGA achieve global optimal point with more decision by smaller generations.