Narrow Results

Self-adaptation has been frequently employed in evolutionary computation. Angeline¹ defined three distinct adaptive levels which are: population, individual and component levels. Cultural Algorithms have been shown to provide a framework in which to model self-adaptation at each of these levels. Here, we examine the role that different forms of knowledge can play in the self-adaptation process at the population level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. An acceptance function using a fuzzy inference engine is employed to select acceptable individuals for forming the generalized knowledge in the belief space. Evolutionary programming is used to implement the population space. The results suggest that the use of a cultural framework can produce substantial performance improvements in execution time and accuracy for a given set of function minimization problems over population-only evolutionary systems.

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures.

Chaotic Ant Swarm (CAS) is an optimization algorithm based on swarm intelligence theory, which has been applied to find the global optimum solution in search space. However, it often loses its effectiveness and advantages when applied to large and complex problems, e.g. those with high dimensions. To resolve the problems of high computational complexity and low solution accuracy existing in CAS, we propose a Disturbance Chaotic Ant Swarm (DCAS) algorithm to significantly improve the performance of the original algorithm. The aim of this paper is achieved by three strategies which include modifying the method of updating ant's best position, neighbor selection method and establishing a self-adaptive disturbance strategy. The global convergence of the DCAS algorithm is proved in this paper. Extensive computational simulations and comparisons are carried out to validate the performance of the DCAS on two sets of benchmark functions with up to 1000 dimensions. The results show clearly that DCAS substantially enhances the performance of the CAS paradigm in terms of computational complexity, global optimality, solution accuracy and algorithm reliability for complex high-dimensional optimization problems.

Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process within cultural systems. In particular, we compare various approaches that use normative and situational knowledge in different ways to guide the function optimization process.

The results in this study demonstrate that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in Evolutionary Programming can produce substantial performance improvements over population-only systems as expressed in terms of (1) systems success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of 34 function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depend on the problem's functional landscape. In addition, it was found that the same held true for the population-only self-adaptive EP systems. Each level of self-adaptation (component, individual, and population) outperformed the others for problems with particular landscape features.

To overcome the poor population diversity and slow convergence rate of grey wolf optimizer (GWO), this paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO). The diversity of grey wolf population is increased and exploration ability is improved. The experiment results of 13 standard benchmark functions indicate that the proposed algorithm has strong global and local search ability, quick convergence rate and high accuracy. EOGWO is also effective and feasible in both low-dimensional and high-dimensional case. Compared to particle swarm optimization with chaotic search (CLSPSO), gravitational search algorithm (GSA), flower pollination algorithm (FPA), cuckoo search (CS) and bat algorithm (BA), the proposed algorithm shows a better optimization performance and robustness.

Based on the GUO’s Algorithm, a high-efficiently hybrid evolutionary algorithm is proposed. The new algorithm has two main characteristics: first, introduce the Gauss mutation operator of Evolution Strategies (ES); second, introduce variable searching subspace. In order to avoid premature of population, the Gauss mutation operator is used; at the same time, for accelerating convergence, the searching subspace can be reduced automatically when the population’s evolutionary value is very close to the global best value of the population. Numerical experiments show that the new algorithm is high-efficiency and the precision of results is very high, at the same time, the experiments’ results of several test functions exceed the best value recoded in the references.

This paper introduces first the concept of distance density, and then proposes a new hybrid genetic algorithm based on distance density and quasi-simplex technique (HGABDDQT). HGABDDQT produces the offspring using the genetic operations and the quasi-simplex technique in parallel. In genetic operations, the crossover probability is determined adaptively by distance density, the mutation probability is determined adaptively by distance density and fitness. No binary encoding/decoding in mutation and crossover operations. HGABDDQT algorithm has been implemented and tested on typical benchmark functions. The experimental study has shown that HGABDDQT is more effective than the competitive algorithm in finding the near global optimal solutions.

This paper discusses the five kinds of swarm intelligence algorithm. On this basis, the interactive user interface simulation of swarm intelligent algorithm optimization function was designed with the function of GUI in MATLAB. This platform has the advantages of friendly interface, simple operation, strong interactivity, visualization, scalability etc. And any continuous function can be simulated, analysed and compared using the algorithm in the platform. At the same time, this paper proposed real coded chaotic quantum genetic algorithm based on catastrophe. The simulation results in the platform and improved algorithms are compared, which can get rid of the understanding of multiple swarm intelligent algorithm in the platform and save time. The simulation results are distinctive and highlight, and it shows that the improved algorithm has high precision, overcoming the premature and the advantages of fast convergence.