Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization

By Hui Liu, Zixing Cai, Yong Wang. Applied Soft Computing, 2010

695 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, background): “in [106], where the DE mutation operator was extended by using two other operators.”
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 ”
Dung beetle optimizer: a new meta-heuristic algorithm for global optimization (2022, methodology): “The optimization results achieved by the DBO algorithm are compared with other techniques including WOA [16], HHO algorithm [17], GWO algorithm [15], CPSO algorithm [45], GSA [46], PSO algorithm [45], MVO algorithm [40], HPSO algo-rithm [47], and PSO-SCA [42].”
The Ant Lion Optimizer (2015, neutral): “The algorithms that are chosen for comparison are: Differential Evolution with Dynamic Stochastic selection (DEDS) [75], Hybrid Particle Swarm Optimization with Differential Evolution (PSODE) [76], Mine Blast Algorithm (MBA) [74], Tsa [77], and CS [72].”