Evolutionary Algorithms for Solving Dynamic Optimization Problems
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Dynamic environments are environments that are capable of changing over time. These changes can occur in various ways, including changes in parameters, target functions, or problem constraints. In this regard, a range of different sciences such as management, economics, computer science, mathematics, etc. have been affected by these changes, both in theory and in practice in the real world. For this reason, solving the problems of dynamic environments that are known as solving dynamic optimization problems has been posed from the past few decades to the present. The most important challenge in...
Dynamic environments are environments that are capable of changing over time. These changes can occur in various ways, including changes in parameters, target functions, or problem constraints. In this regard, a range of different sciences such as management, economics, computer science, mathematics, etc. have been affected by these changes, both in theory and in practice in the real world. For this reason, solving the problems of dynamic environments that are known as solving dynamic optimization problems has been posed from the past few decades to the present. The most important challenge in solving such problems is how to adapt to the newly modified environment. Therefore, the need to trace and track new optimal points (points) in the problem space is felt.
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