Ali R. Al-Roomi

Metropolis biogeography-based optimization

Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm (EA). Although the exploitation level of BBO is good researchers found some weaknesses in its exploration. This study proposes a new hybridization between BBO and simulated annealing (SA) to enhance its performance. In this proposed algorithm, the inferior migrated islands will not be selected unless they pass the Metropolis criterion of SA and so the new algorithm is called MpBBO. The performance of MpBBO is evaluated using 36 benchmark functions with five different cooling strategies of SA and then compared with the original and essentially modified BBO models. The results show the exponential, inverse, and inverse linear cooling strategies provide best solutions, but they are the slowest. Among these three strategies, the exponential cooling rate can compromise between the solution quality and CPU time compared with the others. Also, the inverse cooling rate is competitive and wins when the mutation stage is completely disabled. The F-test and T-test show that MpBBO has significant differences. Further, it has been observed, through sensitivity analysis, that MpBBO behaves like BBO and SA. The parameters of SA and BBO can affect the performance of MpBBO, which has more immunity against trapping into local optimums. Moreover, the superiority of MpBBO appears when it is compared with non-simplified migration-based BBO models as well as other hybrid/non-hybrid EAs.

Economic Load Dispatch Using Hybrid MpBBO-SQP Algorithm

Solving economic load dispatch (ELD) problems of electric power systems means finding the optimal output of the generating units, so that the load demand can be satisfied with the lowest possible operating cost, and without violating any design constraint. Recently, many nature-inspired algorithms have been successfully used to solve these highly constrained non-linear and non-convex ELD problems without facing much efforts as regularly happens with the conventional optimization algorithms. Biogeography-based optimization (BBO) algorithm is a new population-based evolutionary algorithm (EA). As per the conducted studies in the literature, BBO has good exploitation, but it lacks exploration. In this study, the poor exploration level of BBO is enhanced by hybridizing it with the Metropolis criterion of the simulated annealing (SA) algorithm in order to have more control on the migrated individuals; and hence the first phase of this proposed algorithm is called Metropolis BBO (in short MpBBO). The second hybridization phase is done by combining the strength of the Sequential Quadratic Programming (SQP) algorithm with MpBBO to have a new superior algorithm called MpBBO-SQP, where the best solutions per each generation of MpBBO phase is fine-tuned by SQP phase. The performance of MpBBO-SQP is evaluated using three test cases with five different cooling strategies of SA. The results obtained show that MpBBO-SQP outperforms different BBO models as well as many other competitive algorithms presented in the literature.

Optimal coordination of directional overcurrent relays using hybrid BBO/DE algorithm and considering double primary relays strategy

Finding optimal settings of directional overcurrent relays (DOCRs) has been extensively solved by many scenarios with adopting different traditional and modern optimization algorithms. For the sack of simplicity, all the conducted studies in the literature are restricted on a condition that all DOCRs are numerical, static, or electromechanical. There is a fact that when jumping from electromechanical or static relays to numerical relays the former devices could be used as a second wall of protection instead of just throwing them. With a very extreme condition, which may not happen in the real-world applications, all buses will have double primary DOCRs, which where act as backup DOCRs for other buses. That is, the dimension of any given problem is duplicated and becomes very hard to be feasibly and optimally solved. This paper covers this uncommon scenario, and a new hybrid algorithm with some additional sub-algorithms is developed to solve the IEEE 6-bus test system. Moreover, both numerical with electromechanical DOCRs and numerical with static DOCRs are considered in this study.

Estimated economic load dispatch based on real operation logbook

The economic load dispatch (ELD) problem of electric power systems has been solved by many techniques including traditional and modern optimization algorithms. The main problem of achieving this task is that all precise specifications of the generating units are required. Also, in practice, many power systems are operated without considering the ELD strategy due to the lack of experience to deal with this part, which is embedded as a package in the energy management system (EMS), and/or the difficulty of constructing precise constrained objective functions matched with the real generating units. Based on a fact that most power systems maintain their daily records, the estimated economic load dispatch (EELD) can be determined using these recorded datasheets. This novel method can be applied without using any special software, and it is an optimization free technique. Moreover, this technique does not require to determine any parameter nor constraint on the generating units, and all candidate solutions are practical and feasible. The proposed method is tested with a real power system data and it shows encouraging results.