Home » Research Problem in Research Methodology
Research problems are the specific issues, contradictions, or gaps in knowledge that you aim to address in your study. They’re the foundation upon which the entire research project is built, guiding the research questions, objectives, and methodology. Formulating a clear and concise research problem is crucial for the success of any research project, as it sets the direction and focus.
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Here’s how you might approach identifying and articulating a research problem in research methodology
Identification :
It starts with identifying a broad area of interest, such as social sciences, natural sciences, technology, arts, etc. Within this broad area, you’ll need to narrow down to a specific issue or phenomenon that seems to be problematic or inadequately understood.
Background Research :
Conduct a preliminary literature review to understand what has already been discovered about your area of interest. This can highlight existing gaps in knowledge, conflicting findings, or new questions that have emerged.
Problem Statement :
Articulate the problem in a clear, concise statement. This involves specifying the context of the problem, its significance, and why it is worth investigating. The problem statement should be specific enough to be manageable, yet broad enough to be significant.
Justification :
Explain why this problem is important to study. This could be due to its implications for theoretical advancements, societal benefits, or addressing a practical gap. Justifying the problem involves demonstrating the relevance and urgency of finding a solution or deeper understanding.
The research problem is the cornerstone of any academic inquiry, guiding the methodology and analysis. Careful consideration and formulation of the research problem are essential for a successful study.
Differential evolution (DE) algorithm is a classical natural-inspired optimization algorithm which has a good. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to solve the above problems, we proposed an adaptive differential evolution algorithm based on the data processing method and a new mutation strategy (ADEDPMS). In this paper, the data preprocessing method is implemented by k -means clustering algorithm, which is used to divide the initial population into multiple clusters according to the average value of fitness, and select candidate solutions in each cluster according to different proportions. This method improves the quality of candidate solutions of the population to a certain extent. In addition, in order to solve the problem of insufficient global search ability in differential evolution algorithm, we also proposed a new mutation strategy, which is called “DE/current-to- \({p}_{1}\) best& \({p}_{2}\) best”. This strategy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range is in the most promising candidate solution region, and indirectly increases the population diversity of the algorithm. We also proposed an adaptive parameter control method, which can effectively balance the relationship between the exploration process and the exploitation process to achieve the best performance. In order to verify the effectiveness of the proposed algorithm, the ADEDPMS is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO. In the simulation experiment, 6 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed and stability are fully compared. We used ADEDPMS to solve the dynamic economic dispatch (ED) problem with generator constraints. It is compared with the optimization algorithms used to solve the ED problem in the last three years which are AEFA, AVOA, OOA, SCA and TLBO. The experimental results show that compared with the five latest optimization algorithms proposed in the past three years to solve benchmark functions, engineering example problems and the ED problem, the proposed algorithm has strong competitiveness in each test index.
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The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.
This work is supported by the National Natural Science Foundation of China (with number 61906164), by the Natural Science Foundation of Jiangsu Province of China (with number BK20190875).
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School of Information Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
Ruxin Zhao, Wei Wang, Tingting Zhang, Lixiang Fu, Jiajie Kang, Hongtan Zhang, Yang Shi & Chao Jiang
School of Intelligent Manufacturing, Yangzhou Polytechnic Institute, Yangzhou, 225127, Jiangsu, China
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Ruxin Zhao, Wei Wang and Tingting Zhang wrote the main manuscript text. Chang Liu, Jiajie Kang and Lixiang Fu prepared figures and tables. Hongtan Zhang, Shi Yang and Chao Jiang were responsible for editing. All authors reviewed the manuscript.
Correspondence to Ruxin Zhao .
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The authors declared that they have no conflicts of interest to this work.
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Zhao, R., Wang, W., Zhang, T. et al. An Adaptive Differential Evolution Algorithm Based on Data Preprocessing Method and a New Mutation Strategy to Solve Dynamic Economic Dispatch Considering Generator Constraints. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10705-2
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Accepted : 20 August 2024
Published : 08 September 2024
DOI : https://doi.org/10.1007/s10614-024-10705-2
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A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge. Some research will do both of these things, but usually the research problem focuses on one or the other.
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Feasibility: A research problem should be feasible in terms of the availability of data, resources, and research methods. It should be realistic and practical to conduct the study within the available time, budget, and resources. Novelty: A research problem should be novel or original in some way.
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Differential evolution (DE) algorithm is a classical natural-inspired optimization algorithm which has a good. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to ...
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