Modified Differential Evolution Algorithm Research Articles

Modified differential evolution algorithm for optimal power flow with non-smooth cost functions

Differential evolution (DE) is a simple but powerful evolutionary optimization algorithm with continually outperforming many of the already existing stochastic and direct search global optimization techniques. DE algorithm is a new optimization method that can handle non-differentiable, non-linear, and multimodal objective functions. This paper presents an efficient modified differential evolution (MDE) algorithm for solving optimal power flow (OPF) with non-smooth and non-convex generator fuel cost curves. Modifications in mutation rule are suggested to the original DE algorithm, that enhance its rate of convergence with a better solution quality. A six-bus and the IEEE 30 bus test systems with three different types of generator cost curves are used for testing and validation purposes. Simulation results demonstrate that MDE algorithm provides very remarkable results compared to those reported recently in the literature.

Performance of modified differential evolution for optimal design of complex and non-linear chemical processes

Differential evolution (DE) is an evolutionary optimization technique that is exceptionally simple, fast, and robust at numerical optimization. However, the convergence rate of DE in optimizing a computationally expensive objective function still does not meet our requirements, and an attempt to speed up DE is considered necessary. This article introduces a modified differential evolution (MDE) that enhances the convergence rate without compromising the robustness. MDE algorithm utilizes only one set of population array as against two sets in original DE at any given generation. This modification improves the convergence rate of DE and at the same time maintains the robustness. The performance of MDE is evaluated on two benchmark test functions followed by nonlinear chemical processes. The simulation results show empirical evidences on the efficiency and effectiveness of the proposed MDE.

Dynamic Optimization of a Continuous Polymer Reactor Using a Modified Differential Evolution Algorithm

Dynamic optimization of a continuous polymer reactor aims to decide optimal trajectories of control input variables so that the transition time, required to reach the desired steady state from the initial state during startup or grade-change operation, is minimized. The problem is challenging because of its highly nonlinear dynamics and multimodal properties. The proposed modified differential evolution (MDE) algorithm is different from differential evolution algorithms in the sense that MDE employs a local search to enhance the computational efficiency and modified heuristic constraints to systematically reduce the size of the search space. The algorithm is illustrated by several case studies using the dynamic optimization problem of a continuous methyl methacrylate−vinyl acetate copolymerization reactor. The case studies have shown that the proposed algorithm offers faster speed, flexible implementation, and higher robustness to find the global optimum than differential evolution algorithms.