## 1 Introduction
### Contributions and Paper Organization  
In this paper, the concerned ORPD problem with three different standard IEEE systems consisting of 30-, 57-, and 118-buses was solved by ICOA, which was first developed by replacing two lowly effective techniques of standard COA. COA, a novel population-based metaheuristic algorithm, was developed by Juliano and Leandro in 2018 [56]. COA achieved a smaller average, especially for hybrid benchmark functions and cases with high number of variables. However, the real performance of COA for engineering problems such as ORPD has not been demonstrated persuasively, since there has been no application so far. However, COA has not achieved good results for other problems in engineering, and it has been suggested to be modified [57; 58; 59]. For the cases of solving benchmark functions, many functions have real optimal solutions with zero values, and construction of COA has produced solutions around the real ones. In fact, in the first generation of each iteration, COA produced center solutions, and then the new solution was newly produced based on the center ones. The center ones are around the middle point of upper and lower boundaries. The feature unintentionally supports COA to exploit the search space, wherein real global optimum solutions are existing for cases of benchmark functions. Unfortunately, the feature is not effective for the ORPD problem because the real optimum solutions of the ORPD problem are not around the center ones. Therefore, the proposed ICOA method applied the first modification on the first generation by canceling center solutions and using the most effective solution so far. The second modification has been proposed due to low efficiency of the second generation. In the second generation, COA produced one new solution for each pack based on randomization, but there must be selection condition for the randomization. Thus, the second generation is time consuming, but its effectiveness is not high. Basically, the feature is appropriate for the case that approximately all current solutions are lumped in local optimum zones. It can be supported to refresh almost all solutions except the best one, resulting in a high possibility of explore other zones effectively and quickly rather than thank to nature ability. Therefore, in the second modification, we have used another strategy for producing such new solutions of each pack. Thanks to the application of two modifications, the proposed ICOA approach can solve ORPD more effectively and robustly than COA in terms of better stabilization of found solutions as well as finding more effective solutions faster. In addition, the proposed ICOA approach is also competed with other applied metaheuristic algorithms including original ones, improved versions of original ones together with other hybrid methods mentioned above. The IEEE 30-, 57-, and 118-bus power systems with three single objectives, being the TPL, TVD, and \(L\) index, are used for comparison and conclusion. The main contributions of the study can be summarized as follows:  
* Show the structure of conventional COA and indicate its strong points and weak points. Based on found strong and weak points, good features of COA are retained, but bad features are changed by applying modifications,* Simulate the result for the modified method and its conventional method for all study cases to indicate the impact of modifications on the performance of the proposed method,
* Provide a strong optimization approach to get good solutions for the ORPD problem. Power loss, voltage deviation, and voltage stability of the applied systems become better by the solutions provided by the proposed approach,
* The proposed method can solve the ORPD problem faster than other compared methods. In addition to the introduction section, the other parts of the paper are as follows: Description of concerned the ORPD problem is shown in Section 2. The original COA and the proposed ICOA method are explained in detail in Section 3.