Memory Consideration Research Articles

A novel design method for optimal IIR system identification using opposition based harmony search algorithm

In this paper a population based evolutionary optimization methodology called Opposition based Harmony Search Algorithm (OHS) is applied for the optimization of system coefficients of adaptive infinite impulse response (IIR) system identification problem. The original Harmony Search (HS) algorithm is chosen as the parent one and opposition based approach is applied to it with an intention to exhibit accelerated near global convergence profile. During the initialization, for choosing the randomly generated population/solution opposite solutions are also considered and the fitter one is selected as apriori guess for having faster convergence profile. Each solution in Harmony Memory (HM) is generated on the basis of memory consideration rule, a pitch adjustment rule and a re-initialization process which gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic HS algorithm results in balancing of exploration and exploitation of search space. The proposed OHS based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed OHS based system identification approach over GA, PSO and DE in terms of convergence speed, identifying the system plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.

A novel adaptive modified harmony search algorithm to solve multi-objective environmental/economic dispatch

This paper proposes a novel adaptive modification approach based on harmony search algorithm (HS) to solve the multi- objective environmental economic dispatch problem. The proposed algorithm makes use of adaptive formulations to update the adjusting parameters of HS including the pitch adjusting and bandwidth parameters and the harmony memory consideration rate during the optimization process. Meanwhile, a useful modification is proposed to improve the variety of the harmony population effectively. In order to handle both the cost and emission objective functions, the ideas of trapezoidal fuzzy membership function and weighting factor are employed. The satisfying performance of the proposed method is examined through the IEEE standard test system.

Clustering using modified harmony search algorithm

Metaheuristic techniques are being successfully used as optimisation methods in various application areas. This paper presents modification in one such metaheuristic called as harmony search (HS) that is inspired from music improvisation process. The two parameters, harmony memory consideration rate (HMCR) and pitch adjusting rate (PAR), in HS play important role in improvisation of new harmony. Instead of keeping the parameters fixed, as reported in many existing algorithms, these are being allowed to change dynamically during the process of improvisation in the proposed algorithm. This paper further examines the effect on the results when K-means is initialised with solution returned by the proposed algorithm. The effect of harmony memory size has also been investigated on proposed approach. The experiments are performed for data clustering on nine benchmark datasets. The clustering performance of proposed algorithm is compared with K-means, fuzzy C-means, genetic algorithm, and four recently proposed variants of HS. The results are encouraging and demonstrate that the proposed algorithm provides much better values in terms of precision, recall, G-measure, inter-cluster and intra-cluster distances.

A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.

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.

Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

This paper presents a parameter adaptive harmony search algorithm (PAHS) for solving optimization problems. The two important parameters of harmony search algorithm namely Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR), which were either kept constant or the PAR value was dynamically changed while still keeping HMCR fixed, as observed from literature, are both being allowed to change dynamically in this proposed PAHS. This change in the parameters has been done to get the global optimal solution. Four different cases of linear and exponential changes have been explored. The change has been allowed during the process of improvization. The proposed algorithm is evaluated on 15 standard benchmark functions of various characteristics. Its performance is investigated and compared with three existing harmony search algorithms. Experimental results reveal that proposed algorithm outperforms the existing approaches when applied to 15 benchmark functions. The effects of scalability, noise, and harmony memory size have also been investigated on four approaches of HS. The proposed algorithm is also employed for data clustering. Five real life datasets selected from UCI machine learning repository are used. The results show that, for data clustering, the proposed algorithm achieved results better than other algorithms.

A modified harmony search method for environmental/economic load dispatch of real-world power systems

Economic load dispatch (ELD) problem is one of the basic and important optimization problems in a power system. However, considering practical constraints of real-world power systems such as ramp rate limits, prohibited operating zones, valve loading effects, multi-fuel options, spinning reserve and transmission system losses in ELD problem makes it a non-convex optimization problem, which is a challenging one and cannot be solved by traditional methods. Moreover, considering environmental issues, results in combined economic and emission load dispatch (CEELD) problem that is a multiobjective optimization model with two non-commensurable and contradictory objectives. In this paper, a modified harmony search algorithm (MHSA) proposed and applied to solve ELD and CEELD problem considering the abovementioned constraints. In the proposed MHSA, a new improvising method based on wavelet mutation together with a new memory consideration scheme based on the roulette wheel mechanism are proposed which improves the accuracy, convergence speed, and robustness of the classical HSA. Performance of the proposed algorithm is investigated by applying it to solve various test systems having non-convex solution spaces. To Show the effectiveness of the proposed method, obtained results compared with classical harmony search algorithm (HSA) and some of the most recently published papers in the area.

Memetic techniques for examination timetabling

In this paper, we investigate the effectiveness of combining the main components of the memetic algorithms (MAs) on the quality of solutions produced for Uncapacitated Examination Timetabling Problem (UETP). These components are recombination, randomness, and neighbourhood structures. The Harmony Search Algorithm (HSA), which is a variation of MA, is used to perform different combinations of these components. It has three main components: Memory Consideration using the recombination, Random Consideration using the randomness and Pitch Adjustment using the neighbourhood structures (or local search). The combinations among MA components are evaluated using 17 different scenarios each of which reflects a combination of one, two or three components. The results show that the system that combines the three components (recombination, randomness, and neighbourhood structures) provides the best results. Furthermore, the best results obtained from the convergence scenarios were compared with 22 other methods that used a de facto dataset defined by Carter et al. (in Journal of the Operational Research Society 74:373–383, 1996) for UETP. The results exceed those produced by the previous methods in 2 out of 12 datasets.

Hybrid Differential Evolution Harmony Search Algorithm for Power Economic Dispatch Problems

In this paper, a hybrid differential evolution harmony search (HDEHS) algorithm was presented for solving power economic dispatch problems. In this algorithm, mutation and crossover operation instead of harmony memory consideration and pitch adjustment operation, this improved the algorithm convergence rate. Moreover, dynamically adjust the key parameter (e.g. mutagenic factor F, crossover rate CR) to balance the local and global search. Based on a 13 units power system experiment simulations, the HDEHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its three improved algorithms (IHS, GHS and NGHS) that reported in recent literature.

Modified Harmony Search Algorithm for 0-1 Knapsack Problems

In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS).

An analysis of selection methods in memory consideration for harmony search

This paper presents an analysis of some selection methods used in memory consideration of Harmony search (HS) Algorithm. The selection process in memory consideration entails selecting the value of the decision variable from any solution in the Harmony memory (HM). Quite recently, there has been a tendency to adopt novel selection methods that mimic the natural phenomena of the ‘survival of the fittest’ to replace the random selection method in memory consideration. Consequently, the value of decision variable selected using memory consideration is chosen from the higher promising solutions in HM. The adopted selection methods include: proportional, tournament, linear rank, and exponential rank. It has been demonstrated that experimenting with any of these methods in memory consideration directly affects the performance of HS. However, the success of these methods is based on choosing the optimal parameter value of each. The wrong parameter settings might affect the balance between exploration and exploitation of the search space. Accordingly, this paper studies the effect of the selection method parameters in order to show their effect on HS behavior. The evaluation is conducted using standard mathematical functions used in the literature for HS adoptions. The results suggest that the optimal setting of the selection method parameters is crucial to improve the HS performance.

An improved harmony search algorithm to solve economic load dispatch problems with generator constraints

This paper presents a new improved harmony search (IHS) algorithm to solve non-convex economic load dispatch problems in the power system. In the proposed IHS algorithm, multiple harmony memory consideration rates and dynamic pitch adjusting rate are used to generate a new harmony/solution vector with enhanced solution quality. The effectiveness of the proposed IHS algorithm has been successfully tested in the test systems which consists of six units with ramp rate limits, prohibited operating zones and transmission loss, thirteen units with valve point effect, and ten units with multiple fuel options and valve point effect. The results obtained using the proposed IHS algorithm are compared with the results of other techniques reported in the literature. The comparative results reveal that the proposed IHS algorithm has good searching capacity to find the global optimal solution, than the other methods reported in the literature and also has good convergence characteristic.

Analysis of takeover time and convergence rate for harmony search with novel selection methods

Recently, common selection schemes used in harmony search algorithm (HSA) are altered in memory consideration operation to imitate the natural selection principle of survival of the fittest. The selection schemes adopted include: random, proportional, tournament, and linear rank. In this paper, these selection schemes are analysed in order to evaluate their effect on the performance of HSA. The analysis considers takeover time and convergence rate to measure the effectiveness of each selection scheme. Furthermore, a scaled proportional selection scheme is proposed to replace the proportional selection scheme to overcome its shortcoming with negative fitness values. To study the effect of these different selection schemes we use eight global optimisation functions with different characteristics. An experimental evaluation show that linear rank selection provides the highest convergence speed and highest takeover time. On the other hand, scaled proportional selection provides the slowest convergence speed and slowest takeover time. This indicates the effect of the type of the selection method used in memory consideration in takeover time and convergence rate.

An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.

Cellular Harmony Search for Optimization Problems

Structured population in evolutionary algorithms (EAs) is an important research track where an individual only interacts with its neighboring individuals in the breeding step. The main rationale behind this is to provide a high level of diversity to overcome the genetic drift. Cellular automata concepts have been embedded to the process of EA in order to provide a decentralized method in order to preserve the population structure. Harmony search (HS) is a recent EA that considers the whole individuals in the breeding step. In this paper, the cellular automata concepts are embedded into the HS algorithm to come up with a new version called cellular harmony search (cHS). In cHS, the population is arranged as a two-dimensional toroidal grid, where each individual in the grid is a cell and only interacts with its neighbors. The memory consideration and population update are modified according to cellular EA theory. The experimental results using benchmark functions show that embedding the cellular automata concepts with HS processes directly affects the performance. Finally, a parameter sensitivity analysis of the cHS variation is analyzed and a comparative evaluation shows the success of cHS.

Efficient and Accurate Optimal Linear Phase FIR Filter Design Using Opposition-Based Harmony Search Algorithm

In this paper, opposition-based harmony search has been applied for the optimal design of linear phase FIR filters. RGA, PSO, and DE have also been adopted for the sake of comparison. The original harmony search algorithm is chosen as the parent one, and opposition-based approach is applied. During the initialization, randomly generated population of solutions is chosen, opposite solutions are also considered, and the fitter one is selected as a priori guess. In harmony memory, each such solution passes through memory consideration rule, pitch adjustment rule, and then opposition-based reinitialization generation jumping, which gives the optimum result corresponding to the least error fitness in multidimensional search space of FIR filter design. Incorporation of different control parameters in the basic HS algorithm results in the balancing of exploration and exploitation of search space. Low pass, high pass, band pass, and band stop FIR filters are designed with the proposed OHS and other aforementioned algorithms individually for comparative optimization performance. A comparison of simulation results reveals the optimization efficacy of the OHS over the other optimization techniques for the solution of the multimodal, nondifferentiable, nonlinear, and constrained FIR filter design problems.

Global best Harmony Search with a new pitch adjustment designed for Nurse Rostering

AbstractIn this paper, the Harmony Search Algorithm (HSA) is proposed to tackle the Nurse Rostering Problem (NRP) using a dataset introduced in the First International Nurse Rostering Competition (INRC2010). NRP is a combinatorial optimization problem that is tackled by assigning a set of nurses with different skills and contracts to different types of shifts, over a predefined scheduling period. HSA is an approximation method which mimics the improvisation process that has been successfully applied for a wide range of optimization problems. It improvises the new harmony iteratively using three operators: memory consideration, random consideration, and pitch adjustment. Recently, HSA has been used for NRP, with promising results. This paper has made two major improvements to HSA for NRP: (i) replacing random selection with the Global-best selection of Particle Swarm Optimization in memory consideration operator to improve convergence speed. (ii) Establishing multi-pitch adjustment procedures to improve local exploitation. The result obtained by HSA is comparable with those produced by the five INRC2010 winners’ methods.

An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems

Solving the non-convex economic load dispatch (ELD) problem with evolutionary algorithms has gained increasing research in recent years. In this paper, a differential harmony search (DHS) algorithm is proposed by combining the mechanisms of both differential evolution and harmony search. In the DHS, the pitch adjustment operation is cooperated with the different mutation operation to enhance the exploitation ability of harmony search, and both the memory consideration and the pitch adjustment are used to enhance the exploration ability of evolution search. In addition, a repair procedure and three simple selection rules are proposed for constraint handling. Numerical simulations are carried out based on different kinds of testing problems with various constraints including valve point effects, multi-fuels, ramp rate limit and prohibited operation zones. Simulation results and comparisons with the some existing algorithms demonstrate the effectiveness, efficiency and robustness of the proposed DHS algorithm. Finally, the effect of parameter setting on the DHS is investigated as well.

A hybrid harmony search algorithm for ab initio protein tertiary structure prediction

Predicting the tertiary structure of proteins from their linear sequence is a big challenge in biology. The existing computational methods are not powerful enough to search for the precise structure in a huge conformational space. This inadequate capability of the computational methods, however, is a major obstacle when trying to tackle this problem. The observations of some previous studies have revealed much interest in hybridizing a local search-based metahuristic algorithm within the population-based metahuristic algorithm. This study introduces a hybrid harmony search algorithm (HHSA) as a means to solve ab initio protein tertiary structure prediction problem. In HHSA, the iterated local search (ILS) is incorporated with the harmony search algorithm (HSA) to empower it so as to find the local optimal solution within the search space of the new harmony. Furthermore, the global-best concept of particle swarm optimization (PSO) is incorporated in memory consideration as a selection scheme to accelerate the convergence speed. The HHSA predicts the tertiary structure of a protein giving its sequence alone (i.e., from scratch). Our algorithm converges faster than the classical harmony search algorithm. We evaluate our algorithm using two protein sequences. The results show that our algorithm can find more precise solutions than other previous studies.

Effect of Harmony Search Parameters’ Variation in Clustering

Harmony search (HS) is a relatively new meta-heuristic optimization method, which is based on the concept of music improvisation. This paper depicts the impact of constant parameters such as Harmony Memory Consideration Rate and Pitch Adjusting Rate, and presents an approach for parameter tuning. It presents modifications in existing harmony search, by choosing appropriate values of these two parameters and allows them to change dynamically during the process of improvisation. The proposed algorithm has been evaluated for data clustering on five benchmark datasets. The clustering performance of proposed algorithm is compared with K-Means, Genetic algorithm, HS and improved version of HS. Experimental results reveal that proposed algorithm provides better results than the above said techniques in terms of precision, recall, G-Measure, inter-cluster and intra-cluster distance.