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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...
In this paper, an improved DE is proposed to improve optimization performance by implementing three new schemes: sharing mutation, current-to-better mutation and real-random-mutation. When evolution speed is standstill, sharing mutation can increase the search depth, in addition, real-random mutation can disturb individuals and can help individuals diverge to local optimum. When the evolution progresses...
A new strategy of Adaptive Plan System with Genetic Algorithm (APGA) is proposed to reduce a large amount of calculation cost and to improve stability in convergence to an optimal solution for multi-peak optimization problems with multi-dimensions. This is an approach that combines the global search ability of Genetic Algorithm (GA) and the local search ability of Adaptive Plan (AP). The APGA differs...
Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems. A variant of PSO, namely, binary particle swarm optimization (BinPSO) has been previously developed to solve discrete optimization problems. Later, many studies have been done to improve BinPSO in term of convergence speed, stagnation in local optimum, and complexity. In this paper, a novel multi-state...
We present a benchmark for the performance evaluation of heuristic and meta-heuristic approaches to fair distribution of indivisible goods. The specific problem reflected by the benchmark data sets is Wireless Channel Allocation (WCA), and the approach to fair distribution is to choose from feasible allocations by the maximum set of a fairness relation between their corresponding allocation performances...
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...
Performance is of utmost importance for linear algebra libraries since they usually are the core of numerical and simulation packages and use most of the available compute time and resources. However, especially in large scale simulation frameworks the readability and ease of use of mathematical expressions is essential for a continuous maintenance, modification, and extension of the software framework...
The performance of the particle swarm is mainly influenced by individual particles experience and group experience in the period of evolution for particle swarm optimization. To make full use of the two factors and effectively improve the particle swarm optimization performance, Introduced a novel Two-subpopulation Particle Swarm Optimization, The proportion of individual experience and group experiences...
The automatic simultaneous selection of structure and parameters of an artificial neural networks is an important area of research. Although many variants of evolutionary algorithms (EA) have been successfully applied to this problem, their demanding memory requirements have restricted their application to real world problems, especially embedded applications with memory constraints. In this paper,...
Differential evolution (DE) is a simple and efficient evolutionary algorithm. It contains three parameters which need to be predefined by users. These parameters are sensitive to specific problems and difficult to set. Opposition-based computing (OBC) is a new scheme for computational intelligence. OBC is helpful to existing techniques by making better decisions through simultaneous consideration...
In this paper, A self-adaptive strategy to determine the control parameters of Differential Evolution (DE) is proposed based on the elaborate analysis of intrinsic structure. The projection information of fitness function in differential direction is used to get the scale factor, while the difference between the local distance and global search range is applied to determine the crossover rate. This...
An Elite Multi-Group Differential Evolution algorithm for unconstrained single objective optimization is proposed. In the novel algorithm, the population is divided into sub-groups with different parameters setting to balance the global and local search ability. The good information collected in the search process is exchanged among groups. Experiments are conducted on seven commonly used benchmark...
In this paper we present self-adaptive differential evolution algorithm with a small and varying population size on large scale global optimization. The experimental results obtained by our algorithm on benchmark functions provided for the CEC 2012 competition and special session on Large Scale Global Optimization are presented. The experiments were performed on 20 test functions with high dimension...
In this study, the performance of Differential Evolution with landscape modality detection and a diversity archive (LMDEa) is reported on the set of benchmark functions provided for the CEC2012 Special Session on Large Scale Global Optimization. In Differential Evolution (DE), large population size, which is much larger than the number of decision variables in problem to be solved, is adopted in order...
In recent years there has been a growing trend in the application of Memetic Algorithms for solving numerical optimization problems. They are population based search heuristics that integrate the benefits of natural and cultural evolution. In this paper, we propose an Adaptive Memetic Algorithm, named LA-DE which employs a competitive variant of Differential Evolution for global search and Learning...
Adapting genetic operators and parameter settings during the optimization process can improve the overall performance of evolutionary algorithms (EAs). In this paper, a novel maturity-based adaptation strategy for EAs is proposed. During the search process, a maturity degree of the population is calculated based on both the population distribution in the search space and that in the fitness space...
Differential Evolution (DE) is an evolutionary algorithm. DE has been successfully applied to optimization problems including non-linear, non-differentiable, non-convex and multimodal functions. There are several mutation strategies such as the best and the rand strategy in DE. It is known that the best strategy is suitable for unimodal problems and the rand strategy is suitable for multimodal problems...
A new hybrid algorithm based on Differential Evolution (DE) and Particle Swarm Optimization (PSO) is proposed in this paper for dynamic optimization problems. The multi-population strategy is used to enhance the diversity and keeps each subpopulation on a different peak, and then a hybrid operator based on DE and PSO (DEPSO) is designed to find and track the optima for each subpopulation. Using DEPSO...
This paper proposes a hybrid differential evolution algorithm for multi-objective optimization problems. One major feature of this hybrid multi-objective differential evolution (HMODE) algorithm is that it adopts subpopulations whose sizes are dynamically adapted during the evolution process. The second feature is that the HMODE adopts a new solution update mechanism instead of the standard one used...
It is well known that mutation plays a very important role in the successful performance of Differential Evolution (DE) algorithm. The proposed scheme named Modified Random Localization (MRL) is based on strategically selecting the individuals from the entire search space rather than choosing them randomly as in basic DE. The corresponding DE variant named MRL-DE is analyzed on a set of 8 traditional...
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