Full factorial experimental design for parameters selection of Harmony Search Algorithm

AbstractMetaheuristic may be defined as an iterative search process that intelligently performs the exploration and exploitationin the solution space aiming to efficiently find near optimal solutions. Various natural intelligences and inspirations have been artificially embedded into the iterative process. In this work, Harmony Search Algorithm (HSA), which is based on the melody fine tuning conducted by musicians for optimising the synchronisation of the music, was adopted to find optimal solutions of nine benchmarking non-linear continuous mathematical models including two-, three- and four-dimensions. Considering the solution space in a specified region, some models contained a global optimum and multi local optima. A series of computational experiments was used to systematically identify the best parameters of HSA and to compare its performance with other metaheuristics including the Shuffled Frog Leaping (SFL) and the Memetic Algorithm (MA) in terms of the mean and variance ofthe solutions obtained

A hybrid Jaya algorithm for reliability–redundancy allocation problems

© 2017 Informa UK Limited, trading as Taylor & Francis Group. This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching–learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability–redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series–parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30–100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results

CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems

Copyright © 2018 John Wiley & Sons, Ltd. This paper presents a new efficient algorithm inspired by Franklin's and Coulomb's laws theory that is referred to as CFA algorithm, for finding the global solutions of optimal economic load dispatch problems in power systems. CFA is based on the impact of electrically charged particles on each other due to electrical attraction and repulsion forces. The effectiveness of the CFA in different terms is tested on basic benchmark problems. Then, the quality of the CFA to achieve accurate results in different aspects is examined and proven on economic load dispatch problems including 4 different size cases, 6, 10, 15, and 110-unit test systems. Finally, the results are compared with other inspired algorithms as well as results reported in the literature. The simulation results provide evidence for the well-organized and efficient performance of the CFA algorithm in solving great diversity of nonlinear optimization problems

An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm

AbstractThe main goal of the present paper is to present a penalty based cuckoo search (CS) algorithm to get the optimal solution of reliability – redundancy allocation problems (RRAP) with nonlinear resource constraints. The reliability – redundancy allocation problem involves the selection of components' reliability in each subsystem and the corresponding redundancy levels that produce maximum benefits subject to the system's cost, weight, volume and reliability constraints. Numerical results of five benchmark problems are reported and compared. It has been shown that the solutions by the proposed approach are all superior to the best solutions obtained by the typical approaches in the literature are shown to be statistically significant by means of unpaired pooled t-test

KESİKLİ HARMONİ ARAMA ALGORİTMASI İLE OPTİMİZASYON PROBLEMLERİNİN ÇÖZÜMÜ: LİTERATÜR ARAŞTIRMASI

It is usually assumed the variables which are used in the optimization problems are continuous variables. However, the variables have discrete or integer values in many real life practices. Considering discrete integer variables in the optimization problems makes the problems more complex. There are few methods to solve these type of problems. The Harmony Search Algorithm inspired by improvisation of musical harmony and a recent variant of it, The Discrete Harmony Search Algorithm were investigated. It is thought that The usage of the Discrete Harmony Search Algorithm is going to provide a good alternative to solve the optimization problems.Genellikle optimizasyon problemlerinde kullanılan değişkenlerin sürekli değişkenler olduğu kabul edilmektedir. Ancak gerçek hayatta çoğu problemin değişkenleri kesikli veya tam sayı değişkenler şeklindedir. Optimizasyon problemlerinde kesikli tam sayı değişkenlerin dikkate alınmasıyla karmaşıklık daha fazla artmaktadır. Bu tür karmaşık problemlerin çözümünde az da olsa çeşitli yöntemler mevcuttur. Bu çalışmada bir müzik eserinde oluşan harmoniden esinlenilerek geliştirilen Harmoni Arama Algoritması ve henüz yeni bir uygulaması olan Kesikli Harmoni Arama Algoritması ile ilgili yapılan araştırmalar incelenmiştir. Kesikli Harmoni Arama Algoritması kullanılarak optimizasyon problemlerinin çözümü bu konuda bir alternatif sağlayacaktır

An Improved Global Harmony Search Algorithm for the Identification of Nonlinear Discrete-Time Systems Based on Volterra Filter Modeling

This paper describes an improved global harmony search (IGHS) algorithm for identifying the nonlinear discrete-time systems based on second-order Volterra model. The IGHS is an improved version of the novel global harmony search (NGHS) algorithm, and it makes two significant improvements on the NGHS. First, the genetic mutation operation is modified by combining normal distribution and Cauchy distribution, which enables the IGHS to fully explore and exploit the solution space. Second, an opposition-based learning (OBL) is introduced and modified to improve the quality of harmony vectors. The IGHS algorithm is implemented on two numerical examples, and they are nonlinear discrete-time rational system and the real heat exchanger, respectively. The results of the IGHS are compared with those of the other three methods, and it has been verified to be more effective than the other three methods on solving the above two problems with different input signals and system memory sizes

A Novel Discrete Global-Best Harmony Search Algorithm for Solving 0-1 Knapsack Problems

In order to better solve discrete 0-1 knapsack problems, a novel global-best harmony search algorithm with binary coding, called DGHS, is proposed. First, an initialization based on a greedy mechanism is employed to improve the initial solution quality in DGHS. Next, we present a novel improvisation process based on intuitive cognition of improvising a new harmony, in which the best harmony of harmony memory (HM) is used to guide the searching direction of evolution during the process of memory consideration, or else a harmony is randomly chosen from HM and then a discrete genetic mutation is done with some probability during the phase of pitch adjustment. Third, a two-phase repair operator is employed to repair an infeasible harmony vector and to further improve a feasible solution. Last, a new selection scheme is applied to decide whether or not a new randomly generated harmony is included into the HM. The proposed DGHS is evaluated on twenty knapsack problems with different scales and compared with other three metaheuristics from the literature. The experimental results indicate that DGHS is efficient, effective, and robust for solving difficult 0-1 knapsack problems