Population-based Search Methods Research Articles

Hybrid moth-flame optimisation algorithm with differential evolution for visual object tracking

The improvement of the metaheuristic algorithms is one of the exciting topics to investigators in current years to resolve the optimisation and engineering problems. One of the population-based search methods, i.e., moth-flame optimisation algorithm (MFO), is eminent by easy execution, low limits, and high speed. On the other hand, the MFO process has shortcomings; for instance, the verdict of local minimum as a substitute for global minimum and weakness in global pursuit proficiency. In this paper, to resolve these shortages, the MFO algorithm is integrated with differential evolution (DE) and proposed a new hybrid method called MFO-DE. The exploration ability of the MFO algorithm is improved and existence trapped in the local minimum is prohibited by a mixture of the MFO and DE in the MFO-DE algorithm. The proposed algorithm was tested on the set of best-known unimodal and multimodal benchmark functions in various dimensions. Furthermore, MFO-DE is applied to visual object tracking as a real-life application.

Multi-Objective Digital Design Optimization via Improved Drive Granularity Standard Cells

To tackle the complexity of state-of-the-art electronic systems, silicon foundries continuously shrink the technology nodes and electronic design automation (EDA) vendors offer hierarchical design flows to decompose systems into smaller blocks. However, such a staged design methodology consists of various levels of abstraction, where margins will be accumulated and result in degradation of the overall design quality. This limits the full use of capabilities of both the process technology and EDA tools. In this work, a study of drive granularity of standard cells is performed and an interpolation method is proposed for drive option expansion within original cell libraries. These aim to investigate how industrial synthesis tools deal with the drive strength selection using different granularity sets. In addition, a fully-automated, multi-objective (MO) EDA digital flow is introduced for power, performance, area (PPA) optimisation based on drive strength refinement. This population-based search method better handles the increased difficulty of cell selection when using larger logic libraries, producing better optimised solutions than standard tool flow in this case. The achieved experimental results demonstrate how the improved drive granularity cells overall enhance the quality of designs and how a significant improvement in trading off PPA is achieved by the MOEDA flow.

Guest Editorial

Evolutionary Algorithms (EAs) are nature-inspired population-based search methods which work on Darwinian principles of natural selection. Due to their strong search capability and simplicity of implementation, EAs have been successfully applied to solve many complex optimization problems, which cannot be easily solved by traditional mathematical programming approaches, such as linear programming, quadratic programming, and convex optimization. Despite the great success enjoyed by EAs, it is worth noting that existing EA solvers usually conduct the search process from scratch, regardless of how similar the new problem encountered is to those already solved in the past. Therefore, conventional EAs do not learn from previous problems and the search capabilities of the EA solvers do not automatically grow with problem-solving experiences. However, in reality, since problems seldom exist in isolation, solving one problem may thus yield useful information for solving other related problems. In the literature, there is a growing interest in conducting research on evolutionary transfer optimization (ETO) in recent years: a paradigm that integrates EA solvers with knowledge learning and transfer across related domains to achieve better optimization efficiency and performance.

Bi-Objective Workflow Scheduling on Heterogeneous Computing Systems Using a Memetic Algorithm

Due to the high power bills and the negative environmental impacts, workflow scheduling with energy consciousness has been an emerging need for modern heterogeneous computing systems. A number of approaches have been developed to find suboptimal schedules through heuristics by means of slack reclamation or trade-off functions. In this article, a memetic algorithm for energy-efficient workflow scheduling is proposed for a quality-guaranteed solution with high runtime efficiency. The basic idea is to retain the advantages of population-based, heuristic-based, and local search methods while avoiding their drawbacks. Specifically, the proposed algorithm incorporates an improved non-dominated sorting genetic algorithm (NSGA-II) to explore potential task priorities and allocates tasks to processors by an earliest finish time (EFT)-based heuristic to provide a time-efficient candidate. Then, a local search method integrated with a pruning technique is launched with a low possibility, to exploit the feasible region indicated by the candidate schedule. Experimental results on workflows from both randomly-generated and real-world applications suggest that the proposed algorithm achieves bi-objective optimization, improving makespan, and energy saving by 4.9% and 24.3%, respectively. Meanwhile, it has a low time complexity compared to the similar work HECS.

Evolutionary Transfer Optimization - A New Frontier in Evolutionary Computation Research

The evolutionary algorithm (EA) is a nature-inspired population-based search method that works on Darwinian principles of natural selection. Due to its strong search capability and simplicity of implementation, EA has been successfully applied to solve many complex optimization problems, which cannot be easily solved by traditional exact mathematical approaches, such as linear programming, quadratic programming, and convex optimization. Despite its great success, it is worth noting that traditional EA solvers start the search from scratch by assuming zero prior knowledge about the task at hand. However, as problems seldom exist in isolation, solving one problem may yield useful information for solving other related problems. There has been growing interest in conducting research on evolutionary transfer optimization (ETO) in recent years: a paradigm that integrates EA solvers with knowledge learning and transfer across related domains to achieve better optimization efficiency and performance. This paper provides an overview of existing works of ETO based on the type of problems being solved by these methods, which are ETO for Optimization in Uncertain Environment, ETO for Multitask Optimization, ETO for Complex Optimization, ETO for Multi/Many-Objective Optimization, and ETO for Machine Learning Applications. The paper also highlights some of the challenges faced in this emerging research field of computational intelligence and discusses some promising future research directions in ETO. It is hoped that the study presented in this paper can help to inspire the development of more advanced ETO methods and applications.

Firefly-Based Approaches of Image Recognition

The main aim of the reported work is to solve the registration problem for recognition purposes. We introduce two new evolutionary algorithms (EA) consisting of population-based search methods, followed by or combined with a local search scheme. We used a variant of the Firefly algorithm to conduct the population-based search, while the local exploration was implemented by the Two-Membered Evolutionary Strategy (2M-ES). Both algorithms use fitness function based on mutual information (MI) to direct the exploration toward an appropriate candidate solution. A good similarity measure is the one that enables us to predict well, and with the symmetric MI we tie similarity between two objects A and B directly to how well A predicts B, and vice versa. Since the search landscape of normalized mutual information proved more amenable for evolutionary computation algorithms than simple MI, we use normalized mutual information (NMI) defined as symmetric uncertainty. The proposed algorithms are tested against the well-known Principal Axes Transformation technique (PAT), a standard evolutionary strategy and a version of the Firefly algorithm developed to align images. The accuracy and the efficiency of the proposed algorithms are experimentally confirmed by our tests, both methods being excellently fitted to registering images.

Optimization of WEDM parameters for Mg-Zn-RE-Zr alloy using Passing Vehicle Search algorithm

Wire Electric Discharge Machining (WEDM) is a high precision spark machining capable of cutting electrically conductive materials of varying hardness and/or having complex shapes. Since the last decade, focus has increased on magnesium alloys because of their high specific strength and inelasticity, excellent damping properties. The addition of Rare Earth (RE) improves creep and corrosion resistant properties thus rendering them highly useful for several aerospace applications. In the present study, parametric optimization of WEDM on Mg-Zn-RE-Zr alloy is performed using a novel meta-heuristic algorithm-Passing Vehicle Search (PVS). The control parameters considered are current, pulse-on time, pulse-off time and wire feed rate. Response Surface Methodology (RSM) philosophy is implemented through Central Composite Design (CCD) to formulate the mathematical model of Average Surface Roughness (Ra). The adequacy of obtained quadratic model is established using Analysis of Variance (ANOVA). The PVS algorithm employs a population based search method by emulating the algebraic characteristics of vehicles over-taking on a two-lane highway from an initial solution set. Results of optimization through PVS are compared against standard real coded Genetic Algorithm. A remarkable improvement was observed which is critical while considering aerospace standards. This kind of analysis could be useful for production process planning in the aerospace industry.

DE-ForABSA

Today, amongst the various forms of online data, user reviews are very useful in understanding the user's attitude, emotion and sentiment towards a product. In this article, a novel method, named as DE-ForABSA is proposed to forecast automobiles sales based on aspect based sentiment analysis (ABSA) and ClusFuDE [8] (a hybrid forecasting model). DE-ForABSA consists of two phases – first, extracted user reviews of an automobile are analysed using ABSA. In ABSA, the reviews are pre-processed; aspects are extracted & aggregated to determine the polarity score of reviews. Second, uses of ClusFuDE consisting of clustering, fuzzy logical relationships and Differential Evolution (DE) to predict the sales of the automobile. DE is a population-based search method to optimize real values under the control of two operators: mutation & crossover. Score from phase 1 is a parameter in differential mutation in phase 2. The proposed method is tested on reviews & sales data of automobile. The empirical results show a Mean Square Error of 142.90 which indicates an effective consistency of the model

Improved Cross-Entropy-Based Tone Injection Scheme With Structured Constellation Extension Design for PAPR Reduction of OFDM Signals

The application of the cross entropy (CE) method to tone injection (TI) can make the peak-to-average power ratio (PAPR) of the orthogonal frequency division multiplexing (OFDM) signals reduced efficiently with great complexity savings. However, as a population-based search method, the CE method requires large samples to find a high-quality solution, which means higher complexity and more time consuming concerning convergence. To reduce the complexity while achieving promising PAPR performance, an improved CE based TI scheme is proposed in this paper. First, a structured extended constellation for rectangular quadrature amplitude modulation is designed to provide more freedom degrees. With proper parameter selection, the proposed extended constellation can offer more design flexibilities and achieve a better tradeoff between freedom degree and power increase. Then, by analyzing the impact of the subcarrier candidate population on the conventional CE based TI scheme, we derive an explicit expression for the determination of the size of the subcarrier candidate population. And, based on the prior information carried by the clipping signal, proper candidate subcarriers are selected. Finally, an improved CE based TI scheme is proposed, which can efficiently enhance the solution quality and greatly accelerate the convergence speed. Simulation results demonstrate that the improved CE based TI scheme achieves great improvements on the performance of PAPR, bit error rate, power increase and complexity compared with the conventional CE based TI scheme.

Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Since the beginning of cloud computing technology, task scheduling problem has never been an easy work. Because of its NP-complete problem nature, a large number of task scheduling techniques have been suggested by different researchers to solve this complicated optimization problem. It is found worth to employ heuristics methods to get optimal or to arrive at near-optimal solutions. In this work, a combination of two heuristics algorithms was proposed: particle swarm optimization (PSO) and genetic algorithm (GA). Firstly, we list pros and cons of each algorithm and express its best interest to maximize the resource utilization. Secondly, we conduct a performance comparison approach based on two most critical objective functions of task scheduling problems which are execution time and computation cost of tasks in cloud computing. Thirdly, we compare our results with other existing heuristics algorithms from the literatures. The experimental results was examined with benchmark functions and results showed that the particle swarm optimization (PSO) performs better than genetic algorithm (GA) but they both present a similarity because of their population based search methods. The results also showed that the proposed hybrid models outperform the standard PSO and reduces dramatically the execution time and lower the processing cost on the computing resources.

Training of neural network for pattern classification using fireworks algorithm

The challenge of training the artificial neural networks (ANNs) which is frequently used for classification purpose has been consistently growing over the last few years, this is probably due to the high dimensional and multi-modal nature of the search space. Nature-inspired metaheuristic algorithms have been successfully employed in the process of weight training of such complex continuous optimization problems. In this paper, a recently proposed fireworks algorithm (FWA) is presented for the training of the parameters of the ANNs. FWA is a class of population-based search method which imitates the explosion process of real fireworks at night. In order to investigate the performance of the proposed method, experiments were conducted on seven benchmark problem instance from the UCI machine learning laboratory and the results obtained by the proposed method are compared with those obtained by krill herd algorithm, harmony search algorithm and genetic algorithm. The results of the evaluation showed superiority of the proposed algorithm in both SSE and training CA and had comparative performance in testing CA and thus it can be concluded that FWA could be adopted as one of the new template algorithm for the training of ANNs.

Identification of noise in multi noise plant using enhanced version of shuffled frog leaping algorithm

In any factory or industry the high level noise can be very harmful to the employees. As investigated by Occupational Safety and Health Act of 1970, the high level noise not only causes physiological ailments in employees but also causes harmful environment in the neighborhood. Therefore it becomes essential to control the noise levels in any manufacturing plant or industry. This can be achieved by optimal allocation of noise equipment which is quite not easy to recognize the exact location. In this study a shuffled frog-leaping algorithm (SFLA) with modification is applied to identify optimal locations for equipment in order to reduce noise level in multi noise plant. Comparatively, SFLA is a recent addition to the family of nontraditional population based search methods that mimics the social and natural behavior of species (frogs). SFLA merges the advantages of particle swarm optimization and genetic algorithm (GA). Though SFLA has been successfully applied to solve many benchmark and real time problems but it limits in convergence speed. In order to improve its performance, the frog with best position in each memeplexes is allowed to slightly modify its position using random walk. This process improves the local search around the best position. The proposal is named as improved local search in SFLA. The simulated results defend the efficacy of the proposal when compared with the differential evolution, GA and SFL algorithms.

Experimental determination of stiffness and damping in rotating systems using metaheuristic hybrid optimization and state observers

Recently, more research have been conducted on the analysis of the vibration response of rotors, so that new techniques, which are used to characterize the dynamic response of rotating machines, have been developed to aid and predict the lifespan of such systems. One of the vibration management techniques in rotating systems is the monitoring of stiffness and damping parameters of the bearings and system excitation forces. In this context, this paper aims to estimate the stiffness and damping of the bearings through a hybrid metaheuristic method. This uses a population-based search method as a starting point for direct search method, genetic algorithms and quasi-Newton method, respectively, in this work. The function to be minimized contains experimental bearing data. Moreover, the state observer is used to reconstruct all states of the system from the experimental data from the sensors. Thus, it is proposed to identify the excitation forces using orthogonal functions, Chebyshev polynomials specifically, and then, to verify the effectiveness of the method when compared to the calculated force excitation of the system.

Biogeography-based optimization for identifying promising compounds in chemical process

Identifying promising compounds from a vast collection of potential compounds is an important and yet challenging problem in chemical engineering. An efficient solution to this problem will help to reduce the expenditure at the early states of chemical process. In an attempt to solve this problem, the industry is looking for predictive tools that would be useful in testing optimal properties of a candidate compound earlier. This paper explores the application of biogeography-based optimization (BBO) to achieve such predictive work. BBO is a new evolutionary algorithm that is based on the science of biogeography. BBO is a population-based search method that achieves information sharing by species migration. The performance of BBO is compared with genetic algorithm (GA) and particle swarm optimization (PSO) on a set of test functions and the cases of identifying promising compounds. Simulation results show that BBO is a competitive method in determining an optimal solution to the optimization of promising compounds.

Mining association rules using hybrid genetic algorithm and particle swarm optimisation algorithm

Evolutionary computation has become the popular choice for solving complex problems, which are otherwise difficult to solve by traditional methods. Genetic algorithm GA and particle swarm optimisation PSO are both population-based heuristic search methods, which are well suited for mining association rules. GA and PSO both have their unique features and limitations. A hybrid method combining both genetic algorithm and particle swarm optimisation called hybrid GA/PSO GPSO is proposed in this paper. This method is used to bring out the balance between exploration and exploitation, which will result in accurate prediction of the mined association rules and consistency in performance. GA reduces the exploitation tasks and exploration is taken care by PSO. The GPSO methodology for mining association rules performs better than the individual performance of both GA and PSO in terms of predictive accuracy and consistency when tested on five benchmark datasets in the University of California Irvine UCI.

Finding a needle in the blogosphere: An information fusion approach for blog distillation search

In the blogosphere, different actors express their opinions about multiple topics. Users, companies or editors socially interact by commenting, recommending and linking blogs and posts. These social media contents are increasingly growing. As a matter of fact, the size of the blogosphere is estimated to double every six months. In this context, the problem of finding a topically relevant blog to subscribe to becomes a Big Data challenge. Moreover, combining multiple types of evidence is essential for this search task. In this paper we propose a group of textual and social-based signals, and apply different Information Fusion algorithms for a blog distillation search task.Information fusion through the combination of the different types of evidence requires optimisation for appropriately weighting each source of evidence. To this end, we analyse well-established population-based search methods. Namely, global search (Particle Swarm Optimisation and Differential Evolution) and a local search method (Line Search) that has been effective in various Information Retrieval tasks. Moreover, we propose hybrid combinations between the global search and the local search method and compare all the alternatives following a standard methodology. Efficiency is an imperative here and, therefore, we focus not only on achieving high search effectiveness but also on designing efficient solutions.

Urban bicycles renting systems: Modelling and optimization using nature-inspired search methods

Urban Bicycles Renting Systems (UBRS) are becoming a common and useful component in growing modern cities. For an efficient management and support, the UBRS infrastructure requires the optimation of vehicle routes connecting several bicycle base stations and storage centers. In this study, we model this real-world optimization problem as a capacitated Vehicle Routing Problem (VRP) with multiple depots and the simultaneous need for pickup and delivery at each base station location. Based on the VRP model specification, two nature-inspired computational techniques, evolutionary algorithms and ant colony systems, are presented and their performance in tackling the UBRS problem is investigated. In the evolutionary approach, individuals are encoded as permutations of base stations and then translated to a set of routes subject to the constraints related to vehicle capacity and node demands. In the ant-based approach, ants build complete solutions formed of several subtours servicing a subset of base stations using a single vehicle based on both apriori (the attractiveness of a move based on the known distance or other factors) and aposteriori (pheromone levels accumulated on visited edges) knowledge. Both algorithms are engaged for the UBRS problem using real data from the cities of Barcelona and Valencia. Computational experiments for several scenarios support a good performance of both population-based search methods. Comparative results indicate that better solutions are obtained on the average by the ant colony system approach for both considered cities.

Studying the effect of using low-discrepancy sequences to initialize population-based optimization algorithms

In this paper, we investigate the use of low-discrepancy sequences to generate an initial population for population-based optimization algorithms. Previous studies have found that low-discrepancy sequences generally improve the performance of a population-based optimization algorithm. However, these studies generally have some major drawbacks like using a small set of biased problems and ignoring the use of non-parametric statistical tests. To address these shortcomings, we have used 19 functions (5 of them quasi-real-world problems), two popular low-discrepancy sequences and two well-known population-based optimization methods. According to our results, there is no evidence that using low-discrepancy sequences improves the performance of population-based search methods.

A Mixed-Integer Memetic Algorithm Applied to Batch Process Optimization

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a mixed-integer memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and applied to the optimal design of batch processes. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

A Memetic Algorithm for Mixed-Integer Optimization Problems

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on a benchmark mixed-integer constrained optimization problem. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.