Hybrid harmony search algorithm for global optimization

Abstract—This paper proposes two hybrid optimization methods based on Harmony Search algorithm (HS) and two different nature-inspired metaheuristic algorithms. In the first contribution, the combination was between the Improved Harmony Search (IHS) and the Particle Swarm Optimization (PSO). The second contribution merged the IHS with the Differential Evolution (DE) operators. The basic idea of hybridization was to ameliorate all the harmony memory vectors by adapting the PSO velocity or the DE operators in order to increase the convergence speed. The new algorithms (IHSPSO and IHSDE) have been compared to the IHS, DE, PSO and some other algorithms like DHS and HSDM. The DHS and HSDM are two existing algorithms, which use different hybridization concepts between HS and DE. All of these algorithms have been evaluated by different test Benchmark functions. The results demonstrated that the hybrid algorithm IHSDE have the better convergence speed into the global optimum than the IHSPSO and the standard IHS, DE and PSO

Universal approximation propriety of Flexible Beta Basis Function Neural Tree

Abstract — In this paper, the universal approximation propri

A Multi-Agent Architecture for the Design of Hierarchical Interval Type-2 Beta Fuzzy System

This paper presents a new methodology for building and evolving hierarchical fuzzy systems. For the system design, a tree-based encoding method is adopted to hierarchically link low dimensional fuzzy systems. Such tree structural representation has by nature a flexible design offering more adjustable and modifiable structures. The proposed hierarchical structure employs a type-2 beta fuzzy system to cope with the faced uncertainties, and the resulting system is called the Hierarchical Interval Type-2 Beta Fuzzy System (HT2BFS). For the system optimization, two main tasks of structure learning and parameter tuning are applied. The structure learning phase aims to evolve and learn the structures of a population of HT2BFS in a multiobjective context taking into account the optimization of both the accuracy and the interpretability metrics. The parameter tuning phase is applied to refine and adjust the parameters of the system. To accomplish these two tasks in the most optimal and faster way, we further employ a multi-agent architecture to provide both a distributed and a cooperative management of the optimization tasks. Agents are divided into two different types based on their functions: a structure agent and a parameter agent. The main function of the structure agent is to perform a multi-objective evolutionary structure learning step by means of the Multi-Objective Immune Programming algorithm (MOIP). The parameter agents have the function of managing different hierarchical structures simultaneously to refine their parameters by means of the Hybrid Harmony Search algorithm (HHS). In this architecture, agents use cooperation and communication concepts to create high-performance HT2BFSs. The performance of the proposed system is evaluated by several comparisons with various state of art approaches on noise-free and noisy time series prediction data sets and regression problems. The results clearly demonstrate a great improvement in the accuracy rate, the convergence speed and the number of used rules as compared with other existing approaches