Abstract
An advanced hybrid algorithm (haDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) and particle swarm optimization (aPSO) integrated with proposed haDEPSO. In aDE a novel, mutation, crossover and selection strategy is introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The performance of proposed haDEPSO and its component aDE and aPSO are validated on 23 unconstrained benchmark functions, then solved five small (structural engineering) and one large (economic load dispatch)-scale engineering design optimization problems. Outcome analyses confirm superiority of proposed algorithms over many state-of-the-art algorithms.
Highlights
The success of any optimization algorithm majorly depends on its proficiency to solve the complex engineering design optimization problems
According to the mechanical differences, the meta-heuristics algorithms (MAs) can be categorized into four groups as follows: swarm intelligence algorithms (SIAs)—inspired from behavior of social insects or animals, evolutionary algorithms (EAs)—inspired from biology, physics-based algorithms (PBAs)—inspired by the rules governing a natural phenomenon and human behavior-based algorithms (HBAs)—inspired from the human being
We show that proposed algorithms advanced DE (aDE), advanced particle swarm optimization (aPSO) and hybridizing advanced DE and PSO (haDEPSO) have more robust convergence where the results improved as the iterations increased
Summary
The success of any optimization algorithm majorly depends on its proficiency to solve the complex engineering design optimization problems. Most of the design optimization problems in engineering are turning out to be complicated due to involving mixed (discrete and continuous) variables under complex constraints These problems are small- and large-scale nonlinear constrained problems and cannot be solved by traditional methods efficiently. Many conventional optimization algorithms like Newton or quasi-Newton have been developed to solve engineering design optimization problems They have certain inherent drawbacks like high computational complexity, local optimal stagnation and derivation of the search space [1]. To overcome the drawbacks of conventional optimization methods, a bunch of optimization methods known as meta-heuristics algorithms (MAs) has been introduced to solve complex engineering design optimization problems. Motivated by above observations and literature survey, following major contributions have been outlined for solving small- and large-scale engineering design optimization problems. (iii) Designed an advanced hybrid algorithm by hybridizing advanced DE and PSO (haDEPSO: hybridization of aDE and aPSO)
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