Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks

This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable parameters of the WNFN model. The proposed WNFN-S learning scheme is based on dynamic symbiotic evolution (DSE). The proposed DSE uses the sequential-search-based dynamic evolutionary (SSDE) method. In some real-world applications, exact training data may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolutionary learning, called WNFN-R, is proposed. Computer simulations have been conducted to illustrate the performance and applicability of the proposed WNFN-S and WNFN-R learning algorithms.     

Symbiotic artificial immune system

Artificial Immune System algorithms use antibodies that fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm cannot make use of schemata or classes of partial solutions, while sub solutions can help a lot in faster emergence of a totally good solution in many problems. To exploit schemata in artificial immune systems, this paper presents a novel algorithm that combines traditional artificial immune systems and symbiotic combination operator. The algorithm starts searching with partially specified antibodies and gradually builds more and more specified solutions till it finds complete answers. The algorithm is compared with CLONALG algorithm on several multimodal function optimization and combinatorial optimization problems and it is shown that it is faster than CLONALG on most problems and can find solutions in problems that CLONALG totally fails.     

Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm

This paper presents an evolutionary algorithm, called the multi-objective symbiotic evolutionary algorithm (MOSEA), to solve a multi-objective FMS process planning (MFPP) problem with various flexibilities. The MFPP problem simultaneously considers four types of flexibilities related to machine, tool, sequence, and process and takes into account three objectives: balancing the machine workload, minimizing part movements, and minimizing tool changes. The MOSEA is modeled as a two-leveled structure to find a set of well-distributed solutions close to the true Pareto optimal solutions. To promote the search capability of such solutions, two main processes imitating symbiotic evolution and endosymbiotic evolution are introduced, together with an elitist strategy and a fitness sharing scheme. Evolutionary components suitable for the MFPP problem are provided. With a variety of test-bed problems, the performance of the proposed MOSEA is compared with those of existing multi-objective evolutionary algorithms. The experimental results show that the MOSEA is promising in solution convergence and diversity.     

DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization

Incorporation of distributed generation (DG) in distribution network may reduce the network loss if DG of appropriate size is placed at proper strategic location. The current article presents determination of optimal size and location of DG in radial distribution network (RDN) for the reduction of network loss considering deterministic load demand and DG generation using symbiotic organisms search (SOS) algorithm. SOS algorithm is a meta-heuristic technique, inspired by the symbiotic relationship between different biological species. In this paper, optimal size and location of DG are obtained for two different RDNs (such as, 33-bus and 69-bus distribution networks). The obtained results, using the proposed SOS, are compared to the results offered by some other optimization algorithms like particle swarm optimization, teaching-learning based optimization, cuckoo search, artificial bee colony, gravitational search algorithm and stochastic fractal search. The comparison is done based on minimum loss of the distribution network as well as based on the convergence mobility of the fitness function offered by each of the comparative algorithms for both the networks under consideration. It is established that the proposed SOS algorithm offers better result as compared to other optimization algorithms under consideration. The results are also compared to the existing solution available in the literature.     

基于共生进化原理的功能结构设计

从生物的共生进化模式得到启发,从设计认知的高度,系统地总结了基于共生进化原理的共生功能之间的关系,包括“提供作用条件”关系,“消除有害副作用”关系和“消除有害环境影响”关系三类。这些关系为系统地解决创新设计中基于可行原理解的功能结构设计问题奠定了基础。在此基础上提出了集成原理方案设计和功能结构设计的计算机辅助概念设计的进程模型。该进程模型能根据实际创新设计环境以及候选原理解的作用条件、副作用和环境影响,在求解过程中动态地生成待求产品的功能结构,符合设计师的思维习惯。最后给出了模型应用实例。     

Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem

This paper presents the symbiotic organisms search (SOS) heuristic for solving the capacitated vehicle routing problem (CVRP), which is a well-known discrete optimization problem. The objective of CVRP is to decide the routes for a set of vehicles to serve a set of demand points while minimizing the total routing cost. SOS is a simple and powerful metaheuristic that simulates the symbiotic interaction strategies adopted by an organism for surviving in an ecosystem. As SOS is originally developed for solving continuous optimization problems, we therefore apply two solution representations, SR-1 and SR-2, to transform SOS into an applicable solution approach for CVRP and then apply a local search strategy to improve the solution quality of SOS. The original SOS uses three interaction strategies, mutualism, commensalism, and parasitism, to improve a candidate solution. In this improved version, we propose two new interaction strategies, namely competition and amensalism. We develop six versions of SOS for solving CVRP. The first version, SOS_Canonical, utilizes a commonly used continuous to discrete solution representation transformation procedure. The second version is an improvement of canonical SOS with a local search strategy, denoted as SOS_Basic. The third and fourth versions use SR-1 and SR-2 with a local search strategy, denoted as SOS_SR-1 and SOS_SR-2. The fifth and sixth versions, denoted as ISOS_SR-1 and ISOS_SR-2, improve the implementation of SOS_SR-1 and SOS_SR-2 by adding the newly proposed competition and amensalism interaction strategies. The performances of SOS_Canonical, SOS_Basic, SOS_SR-1, and SOS_SR-2 are evaluated on two sets of benchmark problems. First, the results of the four versions of SOS are compared, showing that the preferable result was obtained from SOS_SR-1 and SOS_SR-2. The performances of SOS_SR-1, SOS_SR-2, ISOS_SR-1, and ISOS_SR-2 are then compared, presenting that ISOS_SR-1 and ISOS_SR-2 offer a better performance. Next, the ISOS_SR-1 and ISOS_SR-2 results are compared to the best-known solutions. The results show that ISOS_SR-1 and ISOS_SR-2 produce good VRP solutions under a reasonable computational time, indicating that each of them is a good alternative algorithm for solving the capacitated vehicle routing problem.     

谈抄袭与借鉴在设计中的体现

在现代设计的发展中抄袭和借鉴总是同步发展的,特别是在同一体系中的延续和继承。设计需要借鉴、继承、创新,分析、消耗、提炼、改造、重新组织举一反三。     

Oppositional symbiotic organisms search optimization for multilevel thresholding of color image

Selection of optimal threshold is the most crucial issue in threshold-based segmentation. In case of color image, this task is become challenging, because conventional color image segmentation has computational complexity and also it suffers from lack of accuracy. Various techniques such as threshold based, region growing, edge detection, graph cut, pixel classification, neural network, active contour, gray level co-occurrence matrix are proposed so far for image segmentation in the literature. Out of them, threshold-based segmentation is popular for its simplicity. To address the problem of color image segmentation, we propose an enhanced version of metaheuristic optimization algorithm called Opposition based Symbiotic Organisms Search (OSOS) to solve multilevel image thresholding technique for color image segmentation by introducing opposition based learning concepts to accelerate the convergence rate and enhance the performance of standard symbiotic organisms search (SOS). The performance of the proposed OSOS based algorithm is investigated thoroughly and compared with some existing techniques like Cuckoo Search (CS), BAT algorithm (BAT), artificial bee colony (ABC) and particle swarm optimization (PSO). The comparison is made by applying the algorithm to a set of color images taken from a well-known benchmark dataset (Berkeley Segmentation Dataset (BSDS)) and some of the color images collected for the COCO dataset. It is observed from the results that the performance of the OSOS based algorithm is promising with respect to standards SOS and others in terms of the values of objective functions as well as the values of some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The results of the proposed algorithm may encourage the scientists and engineers to apply it into pattern recognition problems.