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Two equations were integrated to derive a fitness function, which was optimized by two popular optimization methods – particle swarm optimization (PSO) and genetic algorithm (GA).
Popular optimization algorithms were compared and discussed.
Two popular optimization methods are the steepest-gradient descent method and Newton method.
In cheminformatics applications, the most popular optimization strategies are grid search [1, 2] and heuristic choice [3, 4].
The effectiveness of these strategies was compared with the most popular optimization procedures grid search and heuristic choice.
Genetic Algorithms are popular optimization algorithms, often used to solve complex large scale optimization problems in many fields.
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To verify the effectiveness of the model, an empirical study is conducted on a city logistics operator in Beijing and the popular commercial optimization suite IBM ILOG CPLEX is adopted for optimization purpose.
Genetic algorithm (GA) is among the more popular evolutionary optimization techniques.
Designs generated by the modified TLBO algorithm are compared with other popular evolutionary optimization methods.
Different variants of the popular evolutionary optimization techniques are also taken into consideration for comparing them with SGO.
The maximum clique problem (MCP) is one of the most popular combinatorial optimization problems with various practical applications.
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