Research Article
Volume 2 Issue 1
Swati Gade*
December 24, 2022
Abstract
The performance, utilization, reliability, and cost of the system are all improved when optimization techniques are used to solve engineering problems. Researchers have used a number of traditional optimization techniques like geometric programming, nonlinear programming, sequential programming, dynamic programming, etc to solve these problems. Traditional optimization techniques have been effective in many real-world problems, but they have some drawbacks that are primarily caused by the search algorithms they have built into them. The researchers have developed a number of advanced optimization algorithms commonly referred to as metaheuristics, to overcome the limitations of traditional optimization techniques. All of the probabilistic evolutionary and swarm intelligence-based algorithms used to solve optimization problems require common control parameters like population size, generational number, elite size, etc. along with these need their own algorithm-specific control parameters. The effectiveness of these algorithms is significantly influenced by the proper tuning of the algorithm-specific parameters. When tuning algorithm-specific parameters incorrectly, the result is either an increase in computational effort or the local optimal solution. This article presents a review of the application of algorithm-specific parameterless algorithms in electrical engineering applications. This article is expected to play a major role in guiding research scholars in the application of advanced intelligent optimization techniques.
Keywords: advanced optimization techniques; algorithm-specific parameters; Jaya algorithm; Rao algorithm; teaching-learning-based optimization algorithm