Generation Expansion Planning (GEP) is one of the most important decision-making activities in electric utilities. Least-cost GEP is to determine the minimum-cost capacity addition plan (i.e., the type and number of candidate plants) that meets forecasted demand within a pre specified reliability criterion over a planning horizon. In this paper, Differential Evolution (DE), and Oppositionbased ...

Heuristic evolutionary optimization algorithms are the solutions to many engineering optimization problems. Differential evolution (DE) is a real stochastic evolutionary parameter optimization in current use.DE does not require more control parameters compared to other evolutionary algorithms. Master Production Scheduling (MPS) is posed as one of multi objective parameter optimization problems ...

ABSTRACT In many chemical engineering process control applications, one frequently encounters nonlinear optimal control problems. The solution of these types of problems is usually very difficult due to their highly nonlinear, multidimensional and multimodal nature. Several deterministic techniques have been proposed to solve these problems but they are computationally expensive and more likely...

In this paper, we point out some shortcomings of Differential evolution algorithm (DE) with lower search efficiency and advance to the local optimal value easily. Combining with particle swarm algorithm (PSO)’s advantages of convergence rate, we put forward a new hybrid algorithm (DPM) to overcome these shortcomings. Instead of dividing all individuals into two equal size groups, in DPM algorit...

Although differential evolution (DE) algorithms have shown great power in solving continuous optimization problems, it is still a challenging task to design an efficient binary variant of DE algorithm. In this paper, we propose a binary learning differential evolution (BLDE) algorithm, which can efficiently search the feasible region by learning from the obtained solutions. Meanwhile, we also d...

This paper presents the application of Differential Evolution (DE), an Evolutionary Computation method, for the optimization of Thermal Cracking operation. The objective in this problem is the estimation of optimal flow rates of different feeds to the cracking furnace under the restriction on ethylene and propylene production. Thousands of combinations of feeds are possible. Hence an efficient ...

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto–optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. ...

Differential evolution (DE) is an efficient populational meta-heuristic optimization algorithm that has been applied to many difficult real world problems. Due to the relative simplicity of its operations and real encoded data structures, it is very suitable for a parallel implementation on multicore systems and on the GPUs that nowadays reach peak performance of hundreds and thousands of giga ...

Differential evolution algorithm has been widely used, because of its efficient optimization and no complex operation and coding mechanism. But DE falls into the local optimum easily. So this study puts forward a memetic algorithm. The algorithm can increase the diversity of population and jump out the local extreme value point effectively. The convergence speed of the algorithm is improved, be...

Since the introduction of Ant Colony Optimization (ACO) technique in 1992, the algorithm starts to gain popularity due to its attractive features. However, several shortcomings such as slow convergence and stagnation motivate many researchers to stop further implementation of ACO. Therefore, in order to overcome these drawbacks, ACO is proposed to be combined with Differential Evolution (DE) an...