Differential evolution with dynamic stochastic selection for constrained optimization

By Min Zhang, Wenjian Luo, Xufa Wang. Information Sciences, 2008

491 citations recorded by the source corpus.

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Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “been combined with DE [123,124,130], although no actual ranking”
INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, methodology): “= 1 √2𝐴2+𝐴1 𝑃 − 𝜎 ≤ 0, 0 ≤ 𝐴1, 𝐴2 ≤ 1, 𝑙 = 100 𝑐𝑚, 𝑃 = 2 𝑘𝑁 𝑐𝑚2 , 𝜎 = 2 𝑘𝑁 𝑐𝑚2 The problem was optimized using several meta-heuristic algorithms, such as mothflame optimization (MFO) [79], ant lion optimizer (ALO) [77], cuckoo search (CS) [76], differential evolution with dynamic ”
Self-adaptive learning based particle swarm optimization (2011, background): “Unlike many other evolutionary algorithms [2,63,67], no mutation or crossover operators are used.”
The Ant Lion Optimizer (2015, unknown): “The algorithms that are chosen for comparison are: Differential Evolution with Dynamic Stochastic selection (DEDS) [75], Hybrid Particle Swarm Optimization with Differential Evolution (PSO-DE) [76], Mine Blast Algorithm (MBA) [74], Tsa [77], and CS [72].”