An Improved JADE Hybridizing with Tuna Swarm Optimization for Numerical Optimization Problems

Abstract

In this paper, we propose IJADE-TSO, a novel hybrid algorithm in which an improved adaptive differential evolution with optional external archive (IJADE) has been combined with the tuna swarm optimization (TSO). The proposed algorithm incorporates the spiral foraging search and parabolic foraging search of TSO into the mutation strategy in IJADE to improve the exploration ability and population diversity. Additionally, to enhance the convergence efficiency, crossover factor (CR) ranking, CR repairing, top α r1 selection, and population linear reduction strategies have been included in the algorithm. To evaluate the superiority of the proposed algorithm, IJADE-TSO has been benchmarked with its state-of-the-art counterparts using the CEC 2014 test set. Finally, to check the validity of IJADE-TSO, we apply it to photovoltaic (PV) parameter identification and compare its performance with those of other recently developed well-known algorithms. The statistical results reveal that IJADE-TSO outperforms the other compared algorithms.

An Enhanced FCM Clustering Method Based on Multi-Strategy Tuna Swarm Optimization

“…A hybrid model based on the long short-term memory (LSTM) and the TSO algorithm was established to predict wind speed [17]. TSO was blended with improved adaptive differential evolution with an optional external archive to form a new heuristic algorithm, which has been well demonstrated in the problem of photovoltaic parameter identification [18]. An enhanced tuna swarm optimization was proposed for performance optimization of FACTS devices [19].…”