Population-based Metaheuristic Algorithm Research Articles

Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). The MTT problem quickly becomes an NP-hard combinatorial optimization problem. This means that the effort required to solve the MTT problem increases exponentially with the number of tracked objects. In an attempt to find an approximate solution of sufficient quality, several Population-Based Meta-Heuristic (PBMH) algorithms are implemented and tested on simulated optical measurements. These first results show that one of the tested algorithms, namely the Elitist Genetic Algorithm (EGA), consistently displays the desired behavior of finding good approximate solutions before reaching the optimum. The results further suggest that the algorithm possesses a polynomial time complexity, as the computation times are consistent with a polynomial model. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the association and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.

An Algorithm for Continuous Optimization Problems using Hybrid Particle Updating Method

<p>Optimization problem is one such problem commonly encountered in many area of endeavor, obviously due to the need to economize the use of the available resources in many problems. This paper presents a population-based meta-heuristic algorithm for solving optimization problems in a continous space. The algorithm, combines a form of cross-over technique with a position updating formula based on the instantaneous global best position to update each particle position .The algorithm was tested and compared with the standard particle swarm optimization (PSO) on many benchmark functions. The result suggests a better performance of the algorithm over the later in terms of reaching (attaining) the global optimum value (at least for those benchmark functions considered) and the rate of convergence in terms of the number of iterations required reaching the optimum values.</p>

Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm

In this paper, a flexible power system planning strategy using a novel population-based metaheuristic algorithm inspired by the pollination process of flowers named adaptive flower pollination algorithm (APFPA) has been proposed. The proposed power system planning strategy implemented and successfully applied for solving the security optimal power flow (OPF) considering faults at critical generating unit. The main particularity of the proposed variant is that the control variables are optimized based on an adaptive and flexible structure. Also the performances of the standard FPA is improved by dynamically adjusting their control parameters, this allows creating diversity and balance between exploration and exploitation during search process. The robustness of the proposed planning strategy, is demonstrated on the IEEE 30-Bus, and IEEE 57-Bus tests power system for different objectives such as fuel cost, power losses, and voltage deviation. Considering the quality of the obtained results compared with various recent methods reported in the literature, the proposed strategy seems to be a competitive tool for solving with accuracy the security OPF considering critical situations.

Implementation of harmony search on embedded platform

Harmony Search (HS) is relatively a new population-based meta-heuristic optimization algorithm that imitates the music improvisation process of musicians to search for a perfect state of harmony. HS has attracted a lot of attention by showing excellent results for a wide range of optimization problems in diverse fields. HS is typically implemented on a software platform, which restrict its applications to real-time applications. In order to accelerate the algorithm, one can proceed with the parallelization of the algorithm and/or map it directly onto hardware to achieve faster execution time. This paper presents an efficient architecture for parallel HS algorithm in FPGA platform in order to improve HS performance in terms of execution time, resource utilization and power consumption while searching several solution candidates for a problem. The implementation is tested using a suite of well-known benchmark functions. Analysis of the experimental results show that the proposed concurrent implementation has a promising performance up to 175x and no less than 16x as compared with software implementation.

Optimal coordinated design of UPFC and PSS for improving power system performance by using multi-objective water cycle algorithm

This paper presents an optimal design for simultaneously locating unified power flow controller (UPFC) and power system stabilizer (PSS). The parameters of their controllers are also tuned coordinately to enhance the power system stability. A mixed integer nonlinear problem is obtained for the design procedure due to the characteristics of selected objective functions. A new population-based meta-heuristic algorithm, called water cycle algorithm (WCA) is used to solve this problem. The best Pareto optimal set is also attained by defining this problem as a multi-objective function. The simulations results on IEEE 39-bus power system confirm the efficiency and the superior performance of the proposed method when compared with other algorithms.

AFRICAN BUFFALO OPTIMIZATION

This is an introductory paper to the newly-designed African Buffalo Optimization (ABO) algorithm for solving combinatorial and other optimization problems. The algorithm is inspired by the behavior of African buffalos, a species of wild cows known for their extensive migrant lifestyle. This paper presents an overview of major metaheuristic algorithms with the aim of providing a basis for the development of the African Buffalo Optimization algorithm which is a nature-inspired, population-based metaheuristic algorithm. Experimental results obtained from applying the novel ABO to solve a number of benchmark global optimization test functions as well as some symmetric and asymmetric Traveling Salesman’s Problems when compared to the results obtained from using other popular optimization methods show that the African Buffalo Optimization is a worthy addition to the growing number of swarm intelligence optimization techniques. Keywords: Graphite; African Buffalo Optimization; Metaheuristics; population-based; global optimization; Traveling Salesman’s Problem.

A new metaheuristic for continuous structural optimization: water evaporation optimization

The paper proposes a novel physically inspired population-based metaheuristic algorithm for continuous structural optimization called as Water Evaporation Optimization (WEO). WEO mimics the evaporation of a tiny amount of water molecules adhered on a solid surface with different wettability which can be studied by molecular dynamics simulations. A set of six truss design problems from the small to normal scale are considered for evaluating the WEO. The most effective available state-of-the-art metaheuristic optimization methods are used as basis of comparison. The optimization results demonstrate the efficiency and robustness of the WEO and its competitive performance to other algorithms for continuous structural optimization problems.

Sequential Domain Patching for Computationally Feasible Multi-objective Optimization of Expensive Electromagnetic Simulation Models

In this paper, we discuss a simple and efficient technique for multi-objective design optimization of multi-parameter microwave and antenna structures. Our method exploits a stencil-based approach for identification of the Pareto front that does not rely on population-based metaheuristic algorithms, typically used for this purpose. The optimization procedure is realized in two steps. Initially, the initial Pareto-optimal set representing the best possible trade-offs between conflicting objectives is obtained using low-fidelity representation (coarsely-discretized EM model simulations) of the structure at hand. This is realized by sequential construction and relocation of small design space segments (patches) in order to create a path connecting the extreme Pareto front designs identified beforehand. In the second step, the Pareto set is refined to yield the optimal designs at the level of the high-fidelity electromagnetic (EM) model. The appropriate number of patches is determined automatically. The approach is validated by means of two multi-parameter design examples: a compact impedance transformer, and an ultra-wideband monopole antenna. Superiority of the patching method over the state-of-the-art multi-objective optimization techniques is demonstrated in terms of the computational cost of the design process.

An approach to select the optimal process parameters of laser transmission welding using firefly algorithm

In the present work, laser transmission welding process is investigated and optimised using firefly algorithm, a population-based metaheuristic and bio-inspired algorithm. Laser power, welding speed, stand-off distance and clamp pressure are the laser transmission welding process parameters which control the output qualities like lap-shear strength and weld-seam width of the welded part. To select the optimal setting of process parameters, single-objective and multi-objective optimisations are performed using firefly algorithm, in which the objective functions are developed using response surface method. Results obtained by using firefly algorithm are found to be superior to those obtained using conventional optimisation technique. Firefly algorithm also found to be superior to particle swarm optimisation algorithm, differential evolutionary algorithm, and shuffled frog leaping algorithm in terms of convergence rate. It is found that firefly algorithm is an excellent optimisation tool with respect to fast convergence and locating global optima. It is also very efficient in predicting trends of multi-dependent responses.

Evolutionary approaches to signal decomposition in an application service management system

The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a priori. In our approach, the Application Service Management System is treated as a black- or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queue-based computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers.

Hybrid Harmony Search Algorithms

Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. In order to improve exploration or global search ability, exploit local search more effectively, increase convergence speed, improve solution quality, and minimize computational cost, researchers have advanced the concept of hybridizing HSA with other algorithms. This article presents a review of hybrid harmony search algorithms.

A Hybrid Meta-Heuristic Algorithm for Vehicle Routing Problem with Time Windows

Harmony search algorithm, which simulates the musical improvisation process in seeking agreeable harmony, is a population based meta-heuristics algorithm for solving optimization problems. Although it has been successfully applied on various optimization problems; it suffers the slow convergence problem, which greatly hinders its applicability for getting good quality solution. Therefore, in this work, we propose a hybrid meta-heuristic algorithm that hybridizes a harmony search with simulated annealing for the purpose of improving the performance of harmony search algorithm. Harmony search algorithm is used to explore the search spaces. Whilst, simulated annealing algorithm is used inside the harmony search algorithm to exploit the search space and further improve the solutions that are generated by harmony search algorithm. The performance of the proposed algorithm is tested using the Solomon's Vehicle Routing Problem with Time Windows (VRPTW) benchmark. Numerical results demonstrate that the hybrid approach is better than the harmony search without simulated annealing and the hybrid also proves itself to be more competent (if not better on some instances) when compared to other approaches in the literature.

Harmony Search Algorithm and its Variants

In the past three decades nature-inspired and meta-heuristic algorithms have dominated the literature in the broad areas of search and optimization. Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. The concept behind the algorithm is to find a perfect state of harmony determined by aesthetic estimation. This paper starts with an overview of the harmonic phenomenon in music and music improvisation used by musicians and how it is applied to the optimization problem. The concept of harmony memory and its mathematical implementation are introduced. A review of HSA and its variants is presented. Guidelines from the literature on the choice of parameters used in HSA for effective solution of optimization problems are summarized.

Design of heat exchangers using a novel multiobjective free search differential evolution paradigm

Free search (FS) is a recently proposed population-based metaheuristic algorithm, inspired from the animals' behavior. FS can be applied to real value numerical optimization problems, as well as evolutionary algorithms and swarm intelligence techniques. In this paper, a novel multiobjective FS approach combined with differential evolution (MOFSDE) to heat exchanger optimization is presented. Two case studies of heat exchanger design are carried out to illustrate the efficiency of the MOFSDE. Simulation results for the two multiobjective case studies using the proposed MOFSDE are compared with those obtained by using the nondominated sorting genetic algorithm, version II (NSGA-II). The results from this comparison indicate that the MOFSDE performs better than the NSGA-II. The results illustrate that MOFSDE efficiently achieves two goals of multiobjective optimization problems: to find the solutions that converge to an approximated Pareto-front which is well spread, having the advantage of no parameter tuning apart from the population size and the number of generations.

Invasive weed optimization for solving index tracking problems

Index tracking belongs to one of the most important types of problems in portfolio management. In contrast to classical (active) portfolio management and optimization, passive portfolio management usually seeks to replicate a given index for a financial market. Due to transaction costs or legal or other practical limitation on the tradability of the respective assets, such indexes are often not fully replicated by a respective portfolio. Instead, one seeks to use a subset of the index assets (or other types of assets) to obtain a portfolio most similar to the index. This index tracking problem can be formulated as an optimization problem with respect to the minimization of the tracking error. In this article, we explore possibilities to solve the index tracking problem with invasive weed optimization (IWO), a rather new population-based metaheuristics algorithm. The complexity of this real-life problem and especially its solution space and restrictions require substantial adaptation of the original IWO algorithm. We explore different possibilities to adapt IWO to the considered type of problem. The adapted IWO method is tested using MSCI USA Value data, and systematic studies to find suitable parameter values are conducted. Although the method basically works well, the obtained results do not fully reach our intended benchmark. Reasons for that and possibilities for further improvement of the methodology are discussed.

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.

A novel hybrid feature selection method based on rough set and improved harmony search

Feature selection is a process of selecting optimal features that produce the most prognostic outcome. It is one of the essential steps in knowledge discovery. The crisis is that not all features are important. Most of the features may be redundant, and the rest may be irrelevant and noisy. This paper presents a novel feature selection approach to deal with issues of high dimensionality in the medical dataset. Medical datasets are habitually classified by a large number of measurements and a comparatively small number of patient records. Most of these measurements are irrelevant or noisy. This paper proposes a supervised feature selection method based on Rough Set Quick Reduct hybridized with Improved Harmony Search algorithm. Rough set theory is one of the most thriving methods used for feature selection. The Rough Set Improved Harmony Search Quick Reduct (RS-IHS-QR) algorithm is a relatively new population-based meta-heuristic optimization algorithm. This approach imitates the music improvisation process, where each musician improvises their instrument's pitch by searching for a perfect state of harmony. The quality of the reduced data is measured by the classification performance. The proposed algorithm is experimentally compared with the existing algorithms Rough Set Quick Reduct (RS-QR) and Rough Set Particle Swarm Optimization Quick Reduct (RS-PSO-QR). The number of features selected by the proposed method is comparatively low. The proposed algorithm reveals more than 90 % classification accuracy in most of the cases and the time taken to reduct the dataset also decreased than the existing methods. The experimental result demonstrates the efficiency and effectiveness of the proposed algorithm.

A new approach to minimum attribute reduction based on discrete artificial bee colony

The minimum attribute reduction problem in the context of rough set theory is an NP-hard nonlinearly constrained combinatorial optimization problem. In this paper, we propose an efficient and competitive combinatorial artificial bee colony algorithm for solving the minimum attribute reduction problem. In the proposed algorithm, a new multidimensional binary local search scheme for bee colonies based on velocity computation is presented; an employed bee and its recruited onlooker bees use different local search strategies so as to get a possibly more diversified neighboring search around a current food source; the information of the so-far best solution is exploited in various ways by employed bees, onlookers and scouts, respectively; the monotonicity property of classification quality of attribute subsets from the theory of rough sets is employed to avoid possibly invalid local searches. Performance comparisons with some best performing population-based metaheuristic algorithms for the minimum attribute reduction problem were carried out on a number of UCI data sets. The experimental results show that the proposed algorithm overall outperforms all the other algorithms in terms of solution quality and is therefore promising for solving the minimum attribute reduction problem.

On the performance of new local search heuristics for annual crop planning: case study of the Vaalharts irrigation scheme

This paper investigates the capabilities of three new local search (LS) metaheuristic algorithms in determining solutions to an annual crop planning (ACP) problem at an existing Irrigation Scheme. ACP is an optimisation problem in agricultural planning which involves determining resource allocation solutions amongst the various crops that are required to be grown at an irrigation scheme, within a year. The LS algorithms investigated are the best performance algorithm (BPA), the iterative best performance algorithm (IBPA) and the largest absolute difference algorithm (LADA). To determine the relative merits of the solutions found by these algorithms, their solutions have been compared against the solutions of two well-known LS metaheuristic algorithms and four population-based metaheuristic algorithms in the literature. The results show that BPA, IBPA and LADA were competitive in determining solutions for this particular optimisation problem.

Patch-Levy-based initialization algorithm for Bees Algorithm

The Bees Algorithm (BA) is a population-based metaheuristic algorithm inspired by the foraging behavior of honeybees. This algorithm has been successfully used as an optimization tool in combinatorial and functional optimization fields. In addition, its behavior very closely mimics the actual behavior that occurs in nature, and it is very simple and easy to implement. However, its convergence speed to the optimal solution still needs further improvement and it also needs a mechanism to obviate getting trapped in local optima. In this paper, a novel initialization algorithm based on the patch concept and Levy flight distribution is proposed to initialize the population of bees in BA. Consequently, we incorporate this initialization procedure into a proposed enhanced BA variant. The proposed variant is more natural than conventional variants of BA. It mimics the patch environment in nature and Levy flight, which is believed to characterize the foraging patterns of bees in nature. The results of experiments conducted on several widely used high-dimensional benchmarks indicate that our proposed enhanced BA variant significantly outperforms other BA variants and state-of-the-art variants of the Artificial Bee Colony (ABC) algorithm in terms of solution quality, convergence speed, and success rate. In addition, the results of experimental analyses conducted indicate that our proposed enhanced BA is very stable, has the ability to deal with differences in search ranges, and rapidly converges without getting stuck in local optima.