A Reward Population-Based Differential Genetic Harmony Search Algorithm

To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability.

A New Method for Analyzing the Performance of the Harmony Search Algorithm

A harmony search (HS) algorithm for solving high-dimensional multimodal optimization problems (named DIHS) was proposed in 2015 and showed good performance, in which a dynamic-dimensionality-reduction strategy is employed to maintain a high update success rate of harmony memory (HM). However, an extreme assumption was adopted in the DIHS that is not reasonable, and its analysis for the update success rate is not sufficiently accurate. In this study, we reanalyzed the update success rate of HS and now present a more valid method for analyzing the update success rate of HS. In the new analysis, take-k and take-all strategies that are employed to generate new solutions are compared to the update success rate, and the average convergence rate of algorithms is also analyzed. The experimental results demonstrate that the HS based on the take-k strategy is efficient and effective at solving some complex high-dimensional optimization problems.

A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.

Rekomendasi Pemilihan Barang pada Parcel dengan Algoritma Harmony Search

Parcel yang berisi berbagai macam barang, merupakan suatu hadiah yang diberikan kepada seseorang yang memiliki relasi dengan pemberinya. Parcel sering kali dikemas menjelang atau memperingati hari tertentu seperti Natal, Idul Fitri maupun peringatan yang lain. Isi dari parcel bervariasi, dapat berupa makanan, minuman, kue, buah, permen, maupun yang lain. Pada beberapa toko swalayan umumnya menjelang peringatan hari tertentu telah menyediakan Parcel dengan variasi isi di dalamnya dengan harga yang telah ditetapkan. Namun tidak jarang pembeli yang ingin mengemas Parcel mempunyai pandangan yang berbeda dengan isi parcel serta harga yang telah ditetapkan. Untuk memudahkan pencarian isi parcel yang berdasarkan harga tersebut maka digunakan algoritma Harmony Search. Pengembangan pencarian dilakukan dengan pertimbangan ukuran keranjang, kategori barang, serta prosentase masing-masing kategori. Evaluasi nilai aesthetic yang dicari adalah nilai minimum dari selisih harga yang ditetapkan dengan total harga pencarian yang didapatkan. Hasil pengujian dilakukan pada 3 macam harga dengan hasil terbaik didapatkan pada nilai Harmony Memory Consideration Rate(HMCR) 0,95

Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods.