Efficient Differential Evolution algorithms for multimodal optimal control problems

Integrating PSO with modified hybrid GA for solving nonlinear optimal control problems

“…the proposed algorithm is compared with some recently proposed algorithms. These methods consist of SUMT and SQP, proposed in [11], continuous genetic algorithm, CGA [1], (better than SUMT), IPSO-SQP [21], (more accurate than some heuristic algorithms such as GA [22], DE [8], and PSO [15]) and some numerical methods [12,14,29]. Because of the stochastic nature of the proposed algorithm, 10 different runs were made for each result.…”

“…They don't really need good initial guesses and deterministic rules. Some of these methods are; Genetic algorithm (GA), see [1,23,24], Genetic programming (GP), see [18], Particle swarm optimization (PSO), see [3,4,21], Ant colony optimization (ACO), see [27] and Differential evolution (DE), see [8,19,28]. Many authors proposed many types of metaheuristics for solving NOCPs.…”

“…Sim et al [24] combined a GA and the shooting method for solving NOCP. Cruz et al [8] used efficient DE algorithms for solving multi-modal NOCPs. Arumugam and Rao [4] considered the popular GA operator, cross-over and root mean square variants into PSO algorithm to make a faster convergence.…”

Non-linear optimal control of manufacturing system based on modified differential evolution

“…Rakesh made some improvements for DE and applied MDE for optimization of non-linear chemical processes [19]. Lopez Cruz utilized DE algorithm for multimodal optimal control problems [17]. In China, DE algorithm has been applied in many areas, such as chemical industry, finance and optical technology [20] - [22].…”

Optimal approximation of linear systems using Taguchi-sliding-based differential evolution algorithm

“…Here it should be noticed that, from the presented several numerical results, it can be seen that (I) the cases studied by Cheng and Hwang [25] are more complex, complete and detailed than those studied by Jiang et al [24], and (II) the DEA-based method proposed by Cheng and Hwang [25] can provide better outcomes than the DEA-based approach presented by Jiang et al [24]. The advantages of using a DEA for solving the model approximation problems are its global solution finding property, simple but powerful search capability, easy-to-understand concept, compact structure, having only a few control parameters, ease of use, and high convergence characteristics [26][27][28][29][30][31].…”