An improved adaptive differential evolution algorithm for continuous optimization

• pbest roulette wheel selection and pbest retention mechanism are proposed.
• Benchmark and real-world application problems have been studied.
• The experimental results show that the algorithm is competitive.

A novel differential evolution algorithm based on adaptive differential evolution algorithm is proposed by implementing pbest roulette wheel selection and retention mechanism. Motivated by the observation that individuals with better function values can generate better offspring, we propose a fitness function value based pbest selection mechanism. The generated offspring with better fitness function value indicates that the pbest vector of current individual is suitable for exploitation, so the pbest vector should be retained into the next generation. This modification is used to avoid the individual gather around the pbest vector, thus diversify the population. The performance of the proposed algorithm is extensively evaluated both on the 25 famous benchmark functions and four real-world application problems. Experimental results and statistical analyses show that the proposed algorithm is highly competitive when compared with other state-of-the-art differential evolution algorithms.

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