Steven Li

A simplified binary harmony search algorithm for large scale 0-1 knapsack problems

A difference based ingenious improvisation scheme is proposed.The pitch adjustment operator can be executed without requiring any parameters.A greedy repair procedure is introduced to ensure the availability of solutions.The proposed SBHS algorithm is easier for implementation.It is more effective and suitable for solving large scale 0-1 knapsack problems. As an important subset of combinatorial optimization, 0-1 knapsack problems, especially the high-dimensional ones, are often difficult to solve. This study aims to provide a new simplified binary harmony search (SBHS) algorithm to tackle such NP-hard problems arising in diverse research fields. The key difference between SBHS and other HS methods is in the process of improvisation. The differences among harmonies stored in harmony memory rather than the pitch adjustment rate (PAR) and step bandwidth (bw) are employed to produce new solutions and this can greatly alleviate the burden of setting these important factors manually. Moreover, the harmony memory considering rate (HMCR) is dynamically adjusted in terms of the dimension size to improve convergence of the algorithm. Therefore, the proposed method does not require any tedious process of proper parameter setting. To further enhance the population diversity, a specific heuristic based local search around infeasible solutions is carried out to obtain better quality solutions. A set of 10 low dimensional knapsack problems as well as large scale instances with up to 10,000 items are used to test the effectiveness of the proposed algorithm. Extensive comparisons are made with the most well-known state-of-the-art HS methods including 9 continuous versions and 5 binary-coded variants. The results reveal that the proposed algorithm can obtain better solutions in almost all cases and outperforms the other considered HS methods with statistical significance, especially for the large scale problems.

Solving large-scale multidimensional knapsack problems with a new binary harmony search algorithm

Harmony search (HS) is a meta-heuristic method that has been applied widely to continuous optimization problems. In this study, a new binary coded version of HS, named NBHS, is developed for solving large-scale multidimensional knapsack problem (MKP). In the proposed method, focus is given to the probability distribution rather than the exact value of each decision variable and the concept of mean harmony is introduced in the memory consideration. Unlike the existing HS variants which require specifications of parameters such as the pitch adjustment rate and step bandwidth, an ingenious pitch adjustment scheme without parameter specification is executed in the proposed HS according to the difference between two randomly selected harmonies stored in the harmony memory to generate a new candidate harmony. Moreover, to guarantee the availability of harmonies in the harmony memory, a simple but effective repair operator derived from specific heuristic knowledge of the MKP is embedded in the proposed method. Finally, extensive numerical simulations are conducted on two sets of large-scale benchmark problems, and the results reveal that the proposed method is robust and effective for solving the multidimensional knapsack problems with large dimension sizes. HighlightsThe framework of HS is restructured for 0-1 optimization problems.The probability distribution is focused instead of the exact variable value.An ingenious pitch adjustment scheme without parameter specification is executed.A simple but effective repair operator derived from specific heuristic knowledge.This proposed NBHS is robust and effective for solving large-scale MKPs.

Teaching-learning based optimization with global crossover for global optimization problems

Teaching learning based optimization (TLBO) is a newly developed population-based meta-heuristic algorithm. It has better global searching capability but it also easily got stuck on local optima when solving global optimization problems. This paper develops a new variant of TLBO, called teaching learning based optimization with global crossover (TLBO-GC), for improving the performance of TLBO. In teaching phase, a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima. And a new global crossover strategy is incorporated into the learning phase, which aims at balancing local and global searching effectively. The performance of TLBO-GC is assessed by solving global optimization functions with different characteristics. Compared to the TLBO, several modified TLBOs and other promising heuristic methods, numerical results reveal that the TLBO-GC has better optimization performance.

Model penicillin fermentation by least squares support vector machine with tuning based on amended harmony search

Penicillin fermentation is an important part of microbial fermentation. Due to the existence of error date in the independent variables and dependent variables of the penicillin fermentation sample data, the accuracy of the model of penicillin fermentation is affected. In this paper, an amended harmony search (AHS) algorithm is developed to adjust the hyper-parameters of least squares support vector machine (LS-SVM) in order to build penicillin fermentation process model with prediction accuracy. The AHS algorithm is investigated by unconstrained benchmark functions with different characteristics. Compared with other several optimization approaches, AHS demonstrates a better performance. Moreover, using the simulation data from the PenSim simulation platform to validate the effectiveness of the penicillin fermentation process modeling, experiment results show that the penicillin fermentation process modeling based on the tuned LS-SVM by AHS possesses robustness and generalization ability.

Hybrid harmony search particle swarm optimization with global dimension selection

This study presents a hybrid harmony search particle swarm optimization with global dimension selection (HHSPSO-GDS) for improving the performance of particle swarm optimization (PSO). In HHSPSO-GDS, a new global velocity updating strategy is introduced to enhance the neighborhood region search of the current best solution and to get a better trade-off between convergence rate and robustness. Additionally, a dynamic non-linear decreased inertia weight is utilized to balance the global exploration and local exploitation. Moreover, the best-worst improvisation mechanism of harmony search (HS) is implanted in the HHSPSO-GDS algorithm and a global dimension selection is employed in the improvisation process, which can effectively accelerate convergence. Global best information sharing strategy is developed to link the two layer exploration frames (PSO and HS). Finally, a comprehensive experimental study is conducted on a large number of benchmark functions. The experimental results reveal that HHSPSO-GDS performs better in terms of the quality of solution, convergence rate, robustness and scalability compared to various state-of-the-art PSOs and other meta-heuristic search algorithms.

On the iterative convergence of harmony search algorithm and a proposed modification

Inspired by the improvisation process of music players, a population-based meta-heuristic algorithm-harmony search (HS) has been proposed recently. HS is good at exploitation, but it can be poor at exploration, and its convergence performance can also be an issue in some cases. To address these disadvantages, the distance bandwidth (bw) adjusting methods proposed in recent literatures are summarized and the exploration ability of HS improvisation is investigated in this paper. Further, the relationship between improvisation exploration and each parameter under asymmetric interval is derived, and an iterative convergence sufficiency of the iteration equation which consists of variance expectation and mean expectation is proven theoretically. Based on these analyses, a modified harmony search (MHS) algorithm is proposed. Moreover, the effects of the key parameters including HMS, PAR and HMCR on the performance of the MHS algorithm are discussed in depth. Experimental results reveal that the proposed MHS algorithm performs better than HS as well as its state-of-the-art variants and other classic excellent meta-heuristic approaches.

Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

In most research on redundancy allocation problem (RAP), the redundancy strategy for each subsystem is assumed to be predetermined and fixed. This paper focuses on a specific RAP with multiple strategy choices (RAP-MSC), in which both active redundancy and cold standby redundancy can be selected as an additional decision variable for individual subsystems. To do so, the component type, redundancy strategy and redundancy level for each subsystem should be chosen subject to the system constraints appropriately such that the system reliability is maximized. Meanwhile, imperfect switching for cold standby redundancy is considered and a k-Erlang distribution is introduced to model the time-to-failure component as well. Given the importance and complexity of RAP-MSC, we propose a new efficient simplified version of particle swarm optimization (SPSO) to solve such NP-hard problems. In this method, a new position updating scheme without velocity is presented with stochastic disturbance and a low probability. Moreover, it is compared with several well-known PSO variants and other state-of-the-art approaches in the literature to evaluate its performance. The experiment results demonstrate the superiority of SPSO as an alternative for solving the RAP-MSC.

Improved global-best-guided particle swarm optimization with learning operation for global optimization problems

Display Omitted A new population partitioning strategy is employed to PSO algorithm.In the current population, global neighborhood exploration strategy is presented to enhance the global exploration capability.A local learning mechanism is used to improve local exploitation ability in historical best population.Stochastic learning and opposition based learning operations are employed to accelerate convergence speed and improve optimization accuracy in global best population.IGPSO performs better for engineering design optimization problems. In this paper, an improved global-best-guided particle swarm optimization with learning operation (IGPSO) is proposed for solving global optimization problems. The particle population is divided into current population, historical best population and global best population, and each population is assigned a corresponding searching strategy. For the current population, the global neighborhood exploration strategy is employed to enhance the global exploration capability. A local learning mechanism is used to improve local exploitation ability in the historical best population. Furthermore, stochastic learning and opposition based learning operations are employed to the global best population for accelerating convergence speed and improving optimization accuracy. The effects of the relevant parameters on the performance of IGPSO are assessed. Numerical experiments on some well-known benchmark test functions reveal that IGPSO algorithm outperforms other state-of-the-art intelligent algorithms in terms of accuracy, convergence speed, and nonparametric statistical significance. Moreover, IGPSO performs better for engineering design optimization problems.

Improved Harmony Search Algorithm

Display OmittedThe improvisation process of LHS algorithm. Opposition-based learning (OBL) technique is employed in improvisation process. The purpose is to increase the diversity of solution.The current best harmony and worst harmony are used to adjust the parameter BW. An adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space.In the proposed algorithm, a new harmony and its opposite harmony are generated in iteration. Then a competition selection mechanism is established to improve solution precision.The effects that varying the parameter HMS and HMCR have on the performance of the LHS algorithm is also analyzed in detail. In this paper, we propose an improved harmony search algorithm named LHS with three key features: (i) adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space; (ii) opposition-based learning technique is blended to increase the diversity of solution; (iii) competition selection mechanism is established to improve solution precision and enhance the ability of escaping local optima. The performance of the LHS algorithm with respect to harmony memory size (HMS) and harmony memory considering rate (HMCR) are also analyzed in detail. To further evaluate the performance of the proposed LHS algorithm, comparison with ten state-of-the-art harmony search variants over a large number of benchmark functions with different characteristics is carried out. The numerical results confirm the superiority of the proposed LHS algorithm in terms of accuracy, convergence speed and robustness.

The usefulness of derivative-related disclosure: evidence from major Australian banks

In recent years, there has been an increasing public demand for firms, especially financial institutions, to disclose more information related to derivatives due to a series of high profile financial scandals. A number of countries have established accounting and reporting standards for derivative instruments. Limited research on the usefulness and quality of derivative related disclosures are mostly based on the US. This paper examines the usefulness of derivative related disclosure in the Australian banking sector. We first review the policy and requirements for derivative related disclosures in the Australian banking sector. Then we investigate the usefulness of derivative related disclosures based on a sample from major Australian banks. Our preliminary empirical results reveal that the disclosure of principal amounts and credit disclosure appear to be insignificant to stock returns. However, the disclosures of fair gains and losses for both trading and non-trading derivatives are significant to the stock returns.