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In this paper, a novel evolutionary algorithm for many-objective optimization is proposed. The algorithm adopts a new global ranking method to favor convergence and an improved crowding distance to maintain diversity, new elitist selection strategy Based on fitness evaluation is also designed to guide the search towards a representative approximation of the Pareto-optimal front. In order to validate...
Differential evolution (DE) is one of the evolutionally algorithms for solving optimization problems in a continuous space. DE has been widely applied to solve various optimization problems. Additionally, many modified DE algorithms have been developed in an attempt to improve search performance. In this paper, we propose island-based DE with varying subpopulation size. Island model is one of the...
This paper first proposes a simple scheme for adapting the chemotactic step size of the Bacterial Foraging Optimization Algorithm (BFOA), and then this new adaptation and two very popular optimization techniques called Particle Swarm Optimization (PSO) and Differential Evolution (DE) are coupled in a new hybrid approach named Adaptive Chemotactic Bacterial Swarm Foraging Optimization with Differential...
In order to improve the ability of evolution algorithm to solve the complicated combinatorial optimization problems of massive deceptive problems, this paper proposes an improved algorithm which introduces simulated annealing operator to differential evolution algorithm. It aims to enhance the population multiplicity by using the simulated annealing operators' mutation search, and to improve the differential...
Differential evolution (DE) is a kind of evolutionary algorithms (EAs), which are population based heuristic global optimization methods. EAs, including DE, are usually criticized for their slow convergence comparing to traditional optimization methods. How to speed up the EA convergence while keeping its global search ability is still a challenge in the EA community. In this paper, we propose a differential...
During the search process of differential evolution (DE), each new solution may represent a new more promising region of the search space (exploration) or a better solution within the current region (exploitation). This concurrent exploitation can interfere with exploration since the identification of a new more promising region depends on finding a (random) solution in that region which is better...
Recent research has shown for different particle swarm optimization algorithms that unconstrained particles exhibit roaming behavior in that particles leave the boundaries of the search space very early during the search [1], [2]. This results in fruitless search of infeasible space, and will result in particles finding infeasible solutions if better solutions exist outside of the boundaries of the...
Over the last few years, Differential Evolution (DE) algorithms have shown brilliant performance in solving a wide variety of complex optimization problems. However, there is no guarantee that these algorithms will not be trapped in local optima for some problems. In this paper, a DE algorithm is proposed that uses a new mechanism to escape from local optima, during the evolution process by injecting...
A modified differential evolution (DE) algorithm based on opposition based learning and chaotic sequence named Opposition based Chaotic Differential Evolution (OCDE) is proposed. The proposed OCDE algorithm is different from basic DE in two aspects. First is the generation of initial population, which follows Opposition Based Learning (OBL) rules; and the second is: dynamic adaption of scaling factor...
Economic power dispatch (EPD) is an important tool for optimal operation and planning of modern power systems. To solve effectively EPD problems, most of the conventional calculus methods rely on the assumption that the fuel cost characteristic of a generating unit is a continuous and convex function, resulting in inaccurate dispatch. This paper presents the design and application of an enhanced differential...
This paper presents an improved differential evolution based on Gaussian disturbance for multi-objective optimization. Differential evolution algorithm is often trapped in local optima and converges slowly. In this paper, Gaussian disturbance is employed to increase the variety of the individual to improve its performance. External archive is employed to reserve the non-dominated solutions and crowding-distance...
Artificial Bee Colony(ABC) algorithm is a biological-inspired optimization algorithm, which has been shown to be compared with some conventional biological-inspired algorithms, such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Differential Evolution(DE). However, there exists problems such as premature convergence and trapping in local optimal. Inspired by DE, we propose an improved...
Differential evolution (DE) is a popular optimization technique, however it also tends to suffer from premature convergence. One possible way to fix this problem is adaptively to choose the right mutation strategy and control parameter setting for distinct problems. Recently, a new concept, opposition-based learning, was introduced to computational intelligent, which was experimentally proven to be...
To solve the premature convergence problem of the conventional differential evolution, an improved differential evolution algorithm is proposed in this paper. The proposed algorithm introduces mixed distribution mutation operation which combines gaussian distribution mutation and cauchy distribution mutation by a proportion for maintaining the balance of the exploration and exploitation. Experimental...
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