The University Rector welcomed the students participated in the annual student conference

Today, Thursday, the 29th of February, 2024, Prof. Dr. Edrees Muhamad Tahir Harki, Rector of the Erbil Polytechnic University, in the presence of Asst. Prof. Dr. Botan Majid Asngar, the Vice Rector for Scientific Affairs and Higher education, Prof. Dr. Abdulkhaliq Nader, the Dean of the Shaqlawa Technical College and Asst. Prof. Sarsam Khalil Shuani, the Dean of the Erbil Technical Administrative Institute, and Asst. Prof. Dr. Ranj Sirwan Abdullah, the General Director of the Scientific Research Center received several students of Erbil Polytechnic University that participated in the (Annual University Students Conference) at the Ain University in the UAE on 19th and 20th February, participated in the (Annual University Students Conference) at the Ain University in the UAE.

It is worth mentioning that the students of our university participated in four academic projects, they are from the Erbil Technical Engineering College and Erbil Technical Administrative College were: (Farhad Amin Ali under the supervision of Ms. Delvin Hassan, Yad Farhad Hamakhan under the supervision of Ms. Bariza Badie, Mohammed Safin under the supervision of Dr. Rafah Rashid, Zeina Mohammed Jamil Supervised by Ms. Tavga Mahdi Aziz) in the fields of Engineering, Accounting and Marketing.

In the same meeting, the Rector shed light on the importance of such activities to further familiarize the students and teachers of the participated countries with the scientific progress achieved in the Kurdistan Region and Erbil Polytechnic University.

Finally, the Rector presented awards to the participated lecturers and students.

Students of Erbil Polytechnic University participate in an international scientific conference in the UAE

On the 19th and 20th of February, 2024, several students of Erbil Polytechnic University participated in the (Annual University Students Conference) at the Ain University in the UAE.

The Students of our university participated in four academic projects in Erbil Technical Colleges of Administrative and Engineering were (Farhad Amin Ali under the supervision of Ms. Delvin Hassan, Yad Farhad Hamakhan under the supervision of Ms. Bariza Badi, Mohammed Safin under the supervision of Dr. Rafah Rashid, Zeina Mohammed Jamil supervised by Ms. Tavga Mahdi Aziz) in the fields of engineering, accounting and marketing.

Erbil Polytechnic University is an active member of the Union of Arab Universities and the participation of our students in this conference is within the framework of the programs of the Union of Arab Universities.

Metaheuristic Optimization Algorithms in Applied Science and Engineering Applications


  • Azad Abdullah Ameen

  • [email protected]
  • +9647721068874
  • Metaheuristic Optimazation Algorithms in Applied Science and Engineering Applications
  • The objective of this study is to delineate the challenges associated with addressing complex optimization issues, with a specific focus on metaheuristic algorithms. A comprehensive investigation was undertaken to explore the principles and categories of these algorithms to gain a deeper understanding of the issues they present and develop effective strategies to overcome them. To challenge these issues, the study explores metaheuristic algorithms, which are known for their effectiveness in solving such problems. However, these algorithms often struggle with getting stuck in local optima and maintaining a balance between exploration and exploitation. Additionally, they exhibit poor searchability and exploitation performance.

    To address these challenges, this research work introduces three different algorithms: a modified version of child drawing development optimization MCDDO, a hybrid algorithm combining child drawing development optimization with harmony search CDDO-HS, and a novel metaheuristic called the social psychology interaction behavior algorithm SPIBA, inspired by human social psychology interactions.

    The performance of these algorithms is evaluated using various benchmark test functions, including classical and CEC-C06 2019 benchmark functions. Statistical methods, such as ranking and the Wilcoxon rank-sum test, are used to compare the results of these algorithms with the original algorithms, CDDO, HS, and other popular algorithms.

    In the beginning, two different approaches were proposed, namely MCDDO and CDDO-HS. The main objective of both techniques is to overcome the issues that the CDDO faces. The CDDO is an example of a human-based metaheuristic approach that may encounter challenges such as getting trapped in local optima, demonstrating suboptimal performance in the exploration phase, and experiencing stagnation in the nearest optimal solution.

    The first proposed MCDDO incorporates four key mechanisms: iterative pattern memory PM updating during the exploitation phase, where new experiences are compared with the child's current drawings; a change in the primary rule employed during the exploitation phase; parameter tuning to strike a balance between exploration and exploitation phases; and preservation of the best solution obtained in each iteration and comparing new solutions with the best solution during the exploration phase. Following the completion of the evaluation, the statistical findings indicate a consistent superiority of the proposed approach over standard algorithms, as evident in both average and p-value results. Specifically, out of the nineteen classical test functions and ten CEC-2019 benchmark test functions, the proposed approach demonstrated better performance in thirteen and nine instances, respectively. These results were then compared with those obtained from the JAYA, SCA, ChOA, DA, GPSO, and BOA algorithms. The comparative analysis confirmed that the proposed approach outperformed all other metaheuristic algorithms in four out of the ten CEC-2019 benchmark test functions.

    The second proposed method, CDDO-HS, represents a hybridization between CDDO and HS and integrates two crucial mechanisms. Firstly, it relocates the PM to the algorithm's core, updating it with each iteration using the HS algorithm. Secondly, it establishes the PM size at 80% of the overall population, aiming for optimal exploration. After the evaluation, the statistical results reveal that the hybridization approach consistently outperforms standard algorithms in both average and p-value outcomes. Specifically, in comparison with CDDO, it achieves better results in eleven out of nineteen classical test functions and all functions from the CEC-2019 benchmark. When compared with HS, the hybrid approach excels in sixteen out of nineteen classical test functions and seven out of ten CEC-2019 benchmark test functions. These results were then pitted against the ChOA, BOA, FOX, GWO-WOA, WOA-BAT, and DCSO algorithms. The study proved beyond a reasonable doubt that the suggested method is better than all other metaheuristic algorithms in six of the ten CEC-2019 test functions.

    In the subsequent phase, SPIBA, an innovative metaheuristic optimization algorithm inspired by social psychology interaction behavior and social interaction—processes involving the stimulus or response of two or more individuals—was developed. These fundamental ideas have been easily incorporated into SPIBA's core, which operates as a single-object and population-based algorithm. SPIBA's performance was compared to that of the ChOA, BOA, FOX, GWO-WOA, WOA-BAT, and DCSO algorithms. The exploration and convergence measures were utilized to assess its success. Their analytical results definitively indicated that the proposed approach beat all other metaheuristic algorithms in six of ten CEC-2019 benchmark test functions.

    Additionally, SPIBA was applied to equipment real-world engineering and applied science challenges, specifically in pressure vessel design and the analysis of the pathological IgG fraction in the nervous system. When working in pressure vessel design and compared with eight other algorithms—WOA, GWO, FDO, CFDO, WOAGWO, KMGWO, RFSO, and MFDO—SPIBA appeared as the top-performing algorithm. It showed an average solution quality of 6.01E-05 and the lowest standard deviation of 2.00E-04, guaranteeing the ahead position. In the context of the "Nervous System's Pathological IgG Fraction" application problem, a comparison between SPIBA and Leo revealed a significant improvement in the proposed algorithm's performance.


  • Erbil Technical Engineering College

  • Information Systems Engineering

  • AI-Optimization

congratulations

On behalf of the Council of Erbil Technical College, we warmly congratulate the Kurdistan Students Union and the Democratic Youth Union of Kurdistan on the occasion of the 71st anniversary of their founding.
The two organizations played a major role in shaping the future of Kurdistan. The Kurdistan Students Union has been a beacon of knowledge and activity, strengthening young minds and igniting their passion for learning and participation. On the other hand, the Kurdistan Democratic Youth Union supported the rights and aspirations of young people, and called for their voices to be heard and their capabilities to be realized.
Your steadfast commitment to the development of the Kurdish community is truly commendable. Through your tireless efforts, you have raised generations of leaders, intellectuals, and engaged citizens who continue to contribute positively to their communities and the region as a whole.
As you celebrate this important anniversary, I implore you to remain steadfast in your mission and vision. We hope that your organizations will continue to grow and develop in the coming years, to achieve a brighter future for Kurdistan and its youth.
Once again, we congratulate you on this important occasion.
BD Iyad Zaki Saber Agha
Chairman of the Council of Erbil Technical College
February 17,