Ying-ping Chen

Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery

In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 institute of electrical and electronics engineers congress on evolutionary computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system.

Applying Family Competition to Evolution Strategies for Constrained Optimization

This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as a local competition involving the children generated from the same parent, while the selection is a global competition among all of the members in the population. According to our experimental results, the self-adaptation of strategy parameters with deterministic elitist selection may trap ESs into local optima when they are applied to heavy constrained optimization problems. By controlling strategy parameters with non-self adaptive rule, FCES can reduce the computation time of self-adaptive Gaussian mutation, diminish the complexity of selection from (m+1) to (m+m), and avoid to be premature. Therefore, FCES is capable of obtaining better performance and saving the computation time. In this paper, FCES is compared with other evolutionary algorithms on various benchmark problems and the results indicate that FCES is a powerful optimization technique.

Genetic algorithm design inspired by organizational theory: pilot study of a dependency structure matrix driven genetic algorithm

This study proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix (DSM) clustering to extract building block (BB) information and use the information to accomplish BB-wise crossover. Three cases: tight, loose, and random linkage, are tested on both a DSMDGA and a simple genetic algorithm (SGA). Experiments showed that the DSMDGA is able to correctly identify BBs and outperforms a SGA.

Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization

Multi-objective optimization is an essential and challenging topic in the domains of engineering and computation because real-world problems usually include several conflicting objectives. Current trends in the research of solving multi-objective problems (MOPs) require that the adopted optimization method provides an approximation of the Pareto set such that the user can understand the tradeoff between objectives and therefore make the final decision. Recently, an efficient framework, called MOEA/D, combining decomposition techniques in mathematics and optimization methods in evolutionary computation was proposed. MOEA/D decomposes a MOP to a set of single-objective problems (SOPs) with neighborhood relationship and approximates the Pareto set by solving these SOPs. In this paper, we attempt to enhance MOEA/D by proposing two mechanisms. To fully employ the information obtained from neighbors, we introduce a guided mutation operator to replace the differential evolution operator. Moreover, a update mechanism utilizing a priority queue is proposed for performance improvement when the SOPs obtained by decomposition are not uniformly distributed on the Pareto font. Different combinations of these approaches are compared based on the test problem instances proposed for the CEC 2009 competition. The set of problem instances include unconstrained and constrained MOPs with variable linkages. Experimental results are presented in the paper, and observations and discussion are also provided.

Analysis of particle interaction in particle swarm optimization

In this paper, we analyze the behavior of particle swarm optimization (PSO) on the facet of particle interaction. We firstly propose a statistical interpretation of particle swarm optimization in order to capture the stochastic behavior of the entire swarm. Based on the statistical interpretation, we investigate the effect of particle interaction by focusing on the social-only model and derive the upper and lower bounds of the expected particle norm. Accordingly, the lower and upper bounds of the expected progress rate on the sphere function are also obtained. Furthermore, the sufficient and necessary condition for the swarm to converge is derived to demonstrate the PSO convergence caused by the effect of particle interaction.

Analysis on the Collaboration Between Global Search and Local Search in Memetic Computation

The synergy between exploration and exploitation has been a prominent issue in optimization. The rise of memetic algorithms, a category of optimization techniques which feature the explicit exploration-exploitation coordination, much accentuates this issue. While memetic algorithms have achieved remarkable success in a wide range of real-world applications, the key to successful exploration-exploitation synergies still remains obscure as conclusions drawn from empirical results or theoretical derivations are usually quite algorithm specific and/or problem dependent. This paper aims to provide a theoretical model that can depict the collaboration between global search and local search in memetic computation on a broad class of objective functions. In the proposed model, the interaction between global search and local search creates a set of local search zones, in which the global optimal points reside, within the search space. Based on such a concept, the quasi-basin class (QBC) which categorizes problems according to the distribution of their local search zones is adopted. The subthreshold seeker, taken as a representative archetype of memetic algorithms, is analyzed on various QBCs to develop a general model for memetic algorithms. As the proposed model not only well describes the expected time for a simple memetic algorithm to find the optimal point on different QBCs but also consists with the observations made in previous studies in the literature, the proposed model may reveal important insights to the design of memetic algorithms in general.

Designing Efficient Genetic and Evolutionary Algorithm Hybrids

Genetic and evolutionary algorithms (GEAs) are being employed to solve a wide range of problems in search and optimization. Most real-world applications use GEAs in combination with domain specific methods to achieve superior performance. Such combinations, often referred to as hybrids, stand to gain much from a system-level framework for efficiently combining global searchers such as GEAs with domain-specific and local searchers. This chapter presents the foundations for such a framework. The theory herein attempts to attain the optimal division of labor between global and local search so that the desired solution quality can be obtained in the minimum time, or given a fixed time budget, the best solution quality can be obtained. It relies on a two-fold decomposition: the hybrid is composed of a global searcher and a local searcher, and the search space is divided into basins of attraction from where the local search can lead to the desired solution quality. The framework allows us to choose between different schedules so as to maximize chances of success. The framework utilizes knowledge of run duration theory and uses the quality of solution at each generation to compute the parameters needed by the theory. The study also looks at characteristics of a class of functions (known as traps) that determine the speedups that can be obtained from using local search.

Introducing Start Expression Genes to the Linkage Learning Genetic Algorithm

This paper discusses the use of start expression genes and a modified exchange crossover operator in the linkage learning genetic algorithm (LLGA) that enables the genetic algorithm to learn the linkage of building blocks (BBs) through probabilistic expression (PE). The difficulty that the original LLGA encounters is shown with empirical results. Based on the observation, start expression genes and a modified exchange crossover operator are proposed to enhance the ability of the original LLGA to separate BBs and to improve LLGAs performance on uniformly scaled problems. The effect of the modifications is also presented in the paper.

Convergence time for the linkage learning genetic algorithm

This paper identifies the sequential behavior of the linkage learning genetic algorithm (LLGA), introduces the tightness time model for a single building block, and develops the connection between sequential behavior and the tightness time model. By integrating the first building-block model based on sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is then constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by LLGA when solving uniformly scaled problems.

On the optimization of degree distributions in LT code with covariance matrix adaptation evolution strategy

Luby Transform code (LT code) has been a popular and practical technique in the field of channel coding since its proposal. One of the key components of LT code is a degree distribution which is used to determine the relationship between source data and codewords. Luby in his proposal suggested two general methods to construct feasible degree distributions. Such general designs work appropriately in typical situations but not optimally in most cases. To explore the full potential of LT code, in this work, we make the first attempt to introduce evolutionary algorithms to optimize the degree distribution in LT code. Degree distributions are encoded as real-valued vectors and evaluated by numerical simulation of LT code. For applications of different natures, two objectives are implemented to search good degree distributions with different decoding behavior. Compared with the original design, the experimental results are quite promising and demonstrate that the degree distribution can be customized for different purposes. In addition to manually adjusting the degree distribution as the common practice, the work presented in this paper provides an efficient alternative approach to use and adapt LT code for both practitioners and researchers.