Metaheuristic Search Research Articles

Communication Scheduling for Control Performance in TSN-Based Fog Computing Platforms

In this paper we are interested in real-time control applications that are implemented using Fog Computing Platforms consisting of interconnected heterogeneous Fog Nodes (FNs). Similar to previous research and ongoing standardization efforts, we assume that the communication between FNs is achieved via the IEEE 802.1 Time Sensitive Networking (TSN) standard. We model the control applications as a set of real-time flows, and we assume that the messages are transmitted using scheduled traffic that is using the Gate Control Lists (GCLs) in TSN. Given a network topology and a set of control applications, we are interested to synthesize the GCLs for messages such that the Quality-of-Control (QoC) of control applications is maximized and the deadlines of real-time messages are satisfied. We have proposed a Constraint Programming (CP)-based solution to this problem, and developed an accurate analytical model for QoC, which, together with a metaheuristic search employed in the CP solver can drive the search quickly towards good quality solutions. We have evaluated the proposed strategy on several test cases including realistic test cases and also validate the resulted GCLs on a TSN hardware platform and via simulations in OMNET++.

Software test data generation technology based on polymorphic particle swarm evolutionary algorithm

Combinatorial testing is a statute-based software testing method that aims to select a small number of valid test cases from a large combinatorial space of software under test to generate a set of test cases with high coverage and strong error debunking ability. However, combinatorial test case generation is an NP-hard problem that requires solving the combinatorial problem in polynomial time, so a meta-heuristic search algorithm is needed to solve the problem. Compared with other meta-heuristic search algorithms, the particle swarm algorithm is more competitive in terms of coverage table generation scale and execution time. In this paper, we systematically review and summarize the existing research results on generating combinatorial test case sets using particle swarm algorithm, and propose a combinatorial test case generation method that can handle arbitrary coverage strengths by combining the improved one-test-at-a-time strategy and the adaptive particle swarm algorithm for the variable strength combinatorial test problem and the parameter selection problem of the particle swarm algorithm. To address the parameter configuration problem of the particle swarm algorithm, the four parameters of inertia weight, learning factor, population size and iteration number are reasonably set, which makes the particle swarm algorithm more suitable for the generation of coverage tables. For the inertia weights.

A novel stochastic fractal search algorithm with fitness-Distance balance for global numerical optimization

Stochastic Fractal Search (SFS) is a new and original meta-heuristic search (MHS) algorithm with strong foundations. As with many other MHS methods, there are problems in effectively balancing the exploitation-exploration in the SFS algorithm. In order to achieve this balance, it is necessary to improve its diversity capability. This article presents the studies that were carried out to strengthen the diversity and balanced search capabilities of the SFS algorithm. For this purpose, the diversity operator of the SFS algorithm was designed with a novel method called Fitness-Distance Balance (FDB), which more effectively mimics the way fractals occur in nature. Thus, the FDBSFS algorithm, which has a much stronger search performance, was developed. Comprehensive experimental studies were conducted to test and validate the developed FDB-based SFS algorithm (FDBSFS). Thirty-nine novel and powerful MHS algorithms, eighty-nine unconstrained test functions and five constrained engineering problems were used. Two nonparametric tests, the Wilcoxon signed rank test and the Friedman test, were used to analyze the results obtained from the experimental studies. The results of the analysis showed that the problem of premature convergence had been largely eliminated by the application of the FDB method and that the exploitation-exploration balance was also effectively provided. Moreover, the proposed FDBSFS algorithm ranked first among the thirty-nine competing algorithms.

BMFO-SIG: A Novel Binary Moth Flame Optimizer Algorithm with Sigmoidal Transformation for Combinatorial Unit Commitment and Numerical Optimization Problems

To achieve paramount economy, hybrid renewable energy sources are getting importance, as renewable sources are cost less. Over past few years, wind energy incorporation drew more consideration in electrical market, as wind power acting affirmative role in energy saving as well as sinking emission pollutants. Moth-flame optimizer is recently proposed meta-heuristics search algorithm, which is encouraged by direction-finding nature of moth and their convergence towards light. Albeit, moths are having a strong ability to maintain a fixed angle with respect to the moon and possess an effective mechanism for travelling in a straight line for long distances. However, they are ensnared in a deadly/useless spiral path around imitation source of lights. In proposed research, to improve the performance of moth-flame optimizer around deadly corkscrew path around imitation source of lights, for discrete optimization problems, two binary variants has been implemented and tested for 23 benchmark functional including combinatorial unit commitment problem. This paper critically analyze per megawatt cost saving while incorporating wind energy sources along with thermal units. The searches for allocation of generators (units that participate in generation to take up load) and once units are decided allocation of generations (economic load dispatch) is done by mixed integer quadratic programming (MIQP). To verify the feasibility and efficacy of operation of proposed algorithms, small- and medium-scale power systems consisting of 5-, 6-, 10-, 20- and 40-generaing unit systems taken into consideration. Commitment and scheduling pattern has been evaluated with and without wind integration and it has been experimentally found that proposed algorithm gives superior type of solutions as compared to other recently reported meta-heuristics search algorithms.

Comparison of three novel hybrid metaheuristic algorithms for structural optimization problems

Computational efficiency of metaheuristic optimization algorithms depends on appropriate balance between exploration and exploitation. An important concern in metaheuristic optimization is that there is no guarantee that new trial designs will always improve the current best record. In this regard, there not exist any metaheuristic algorithm inherently superior over all other methods. This study compares three advanced formulations of state-of-the-art metaheuristic optimization algorithms – Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) – including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new formulations is to generate high quality trial designs lying on a properly chosen set of descent directions. This is done throughout the optimization process. Besides hybridizing the metaheuristic search engines of HS/BBBC/SA with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. All these enhancements allow to approach more quickly the region of design space hosting the global optimum.The new algorithms are tested in four weight minimization problems of skeletal structures and three mechanical/civil engineering design problems with up to 204 continuous/discrete variables and 20,070 nonlinear constraints. All test problems may contain multiple local minima. The optimization results and an extensive comparison with the literature clearly demonstrate the validity of the proposed approach which allows to significantly reduce the number of function evaluations/structural analyses with respect to the literature and improves robustness of metaheuristic search engines.

An Automatic Blocking Keys Selection For Efficient Record Linkage

One of the important processes in the data quality field is record linkage (RL). RL (also known as entity resolution) is the process of detecting duplicates that refer to the same real-world entity in one or more datasets. The most critical step during the RL process is blocking, which reduces the quadratic complexity of the process by dividing the data into a set of blocks. By that way, matching is done only between the records in the same block. However, selecting the best blocking keys to divide the data is a hard task, and in most cases, it's done by a domain expert. In this paper, a novel unsupervised approach for an automatic blocking key selection is proposed. This approach is based on the recently proposed meta-heuristic bald eagles search (bes) optimization algorithm, where the problem is treated as a feature selection case. The obtained results from experiments on real-world datasets showed the efficiency of the proposition where the BES for feature selection outperformed existed approaches in the literature and returned the best blocking keys.

Evaluation of EHO Algorithm’s in solving various real-life problems

Abstract: This paper, presents a new swarm-based metaheuristic search algorithm, known as Elephant Herding Optimization (EHO), which is proposed for solving various daily real-life problems such as benchmark problems, Service Selection in QoS-Aware Web Service Composition, Energy-Based Localization, PID controller tuning, Appliance Scheduling in Smart Grid identification and other problems. The EHO method is inspired by the herding behavior of elephant group. In nature, elephants live in clans under the leadership of a matriarch (female elephant), while the male elephants separate from their family when they grow up. These two behaviors are used by EHO as two operators: clan updating operator and separating operator, which will be used in the optimizing process. Elephant Herding Optimization (EHO) is used to solve NP-Hard problems.

TacTok: semantics-aware proof synthesis

Formally verifying software correctness is a highly manual process. However, because verification proof scripts often share structure, it is possible to learn from existing proof scripts to fully automate some formal verification. The goal of this paper is to improve proof script synthesis and enable fully automating more verification. Interactive theorem provers, such as the Coq proof assistant, allow programmers to write partial proof scripts, observe the semantics of the proof state thus far, and then attempt more progress. Knowing the proof state semantics is a significant aid. Recent research has shown that the proof state can help predict the next step. In this paper, we present TacTok, the first technique that attempts to fully automate proof script synthesis by modeling proof scripts using both the partial proof script written thus far and the semantics of the proof state. Thus, TacTok more completely models the information the programmer has access to when writing proof scripts manually. We evaluate TacTok on a benchmark of 26 software projects in Coq, consisting of over 10 thousand theorems. We compare our approach to five tools. Two prior techniques, CoqHammer, the state-of-the-art proof synthesis technique, and ASTactic, a proof script synthesis technique that models proof state. And three new proof script synthesis technique we create ourselves, SeqOnly, which models only the partial proof script and the initial theorem being proven, and WeightedRandom and WeightedGreedy, which use metaheuristic search biased by frequencies of proof tactics in existing, successful proof scripts. We find that TacTok outperforms WeightedRandom and WeightedGreedy, and is complementary to CoqHammer and ASTactic: for 24 out of the 26 projects, TacTok can synthesize proof scripts for some theorems the prior tools cannot. Together with TacTok, 11.5% more theorems can be proven automatically than by CoqHammer alone, and 20.0% than by ASTactic alone. Compared to a combination of CoqHammer and ASTactic, TacTok can prove an additional 3.6% more theorems, proving 115 theorems no tool could previously prove. Overall, our experiments provide evidence that partial proof script and proof state semantics, together, provide useful information for proof script modeling, and that metaheuristic search is a promising direction for proof script synthesis. TacTok is open-source and we make public all our data and a replication package of our experiments.

Chaos based optics inspired optimization algorithms as global solution search approach

Abstract Metaheuristic optimization algorithms are efficiently used in many large-scale complex problems. Recently, a physics-based metaheuristic search and optimization method entitled Optics Inspired Optimization (OIO) has been proposed. OIO treats the search field of the interested problem to be optimized as a wavy mirror in which the concave mirror is represented as a valley and the convex mirror is represented as a peak. Each candidate solution represents an artificial light point. OIO is a very new metaheuristic method and different approaches should be integrated to obtain a faster convergence with high accuracy by balancing the exploitation and exploration. This paper is the first work on performance improvement of this method by preventing the falling into local optimum solutions and slow convergence speed. In this article, different ergodic chaotic systems are used for the first time to generate chaotic values instead of random values in OIO processes in order to enhance the global convergence speed and prevent stuck on local solutions of classical OIO algorithm. For this purpose, three new enhanced OIO methods are proposed. Furthermore, a new application area for chaos is proposed. The chaotic OIO algorithms proposed in this study are tested in unconstrained benchmark problems and constrained real-world engineering problems. Promising results are obtained from the detailed simulations.

Constrained Mixed-Variable Design Optimization Based on Particle Swarm Optimizer with a Diversity Classifier for Cyclically Neighboring Subpopulations

In this research, an easy-to-use particle swarm optimizer (PSO) for solving constrained engineering design problems involving mixed-integer-discrete-continuous (MIDC) variables that adopt two kinds of diversity-enhancing mechanisms to achieve superior reliability and validity was developed. As an initial diversity-boosting tool, the local neighborhood topology of each particle is set up such that information exchange is restricted to a limited number of consecutively numbered particles. This topological mechanism forces each particle to move in the search space while interacting only with its neighboring subpopulation. The second diversity-enhancing task is to ensure that the exploration behavior of each particle in the search space is governed such that it follows the diversity classifier decision applied to its subpopulation. This diversity classification iteratively adjusts the three-phase velocity-related mechanism of each particle such that it approaches or retreats from its previous best position/the current best position among the subpopulation. In summary, this PSO tool not only introduces the social interaction of the particle within its cyclically neighboring subpopulation but also exploits the three-phase velocity behavior law governed by the distributed diversity measures categorized for each neighboring subpopulation. This scheme has superior reliability, as well as high practicality for engineering optimization problems involving MIDC variables, which are handled by the widely adopted straightforward rounding-off technique used in most swarm-inspired metaheuristic search technologies.

Metaheuristic Optimization of Insulin Infusion Protocols Using Historical Data with Validation Using a Patient Simulator

Metaheuristic search algorithms are used to develop new protocols for optimal intravenous insulin infusion rate recommendations in scenarios involving hospital in-patients with Type 1 Diabetes. Two metaheuristic search algorithms are used, namely, Particle Swarm Optimization and Covariance Matrix Adaption Evolution Strategy. The Glucose Regulation for Intensive Care Patients (GRIP) serves as the starting point of the optimization process. We base our experiments on a methodology in the literature to evaluate the favorability of insulin protocols, with a dataset of blood glucose level/insulin infusion rate time series records from 16 patients obtained from the Waikato District Health Board. New and significantly better insulin infusion strategies than GRIP are discovered from the data through metaheuristic search. The newly discovered strategies are further validated and show good performance against various competitive benchmarks using a virtual patient simulator.

Combining metaheuristic search and simulation to deal with capacitated aisles in facility layout

The layout of machines in a manufacturing system has a significant impact on production time and cost. The aim of a good layout is generally determining the machines’ position on the shop floor to minimize transportation time and cost. However, not only machines’ positions but also aisle affect transportation cost and time. The aisles are paths that transporters go through them to move the materials between machines. The capacity of the aisles is limited to a specific number of transporters and this may cause transporters to wait before being allowed to pass an aisle. Therefore, when optimizing the layout of machines, the aisles structure and their capacity must be considered. This article presents a hybrid approach for layout design in manufacturing systems taking into account capacitated aisles structure. The proposed approach combines a metaheuristic algorithm and simulation. First, the aisle structure is defined and then, the layout of the machines is determined. Each layout is evaluated through a simulation model. In this way, it is possible to avoid unrealistic assumptions and consider realistic conditions such as stochastic demand, random process time and random transportation time. Finally, a numerical example is included to illustrate the proposed approach.

Pareto optimal synthesis of eight-bar mechanism using meta-heuristic multi-objective search approaches: application to bipedal gait generation

The structural complexity of the bipedal locomotion may be reduced by using mechanisms with fewer actuators, which ideally do not degrade the performance and functionality of the robot. However, when the mechanism design considers conflicting design objectives, the search for design solutions with specific trade-offs becomes hard. This work studies different multi-objective search approaches the dominance-based, the decomposition-based and the metric-driven in the dimensional synthesis of the eight-bar mechanism for the bipedal locomotion application in the sagittal plane stated as a mixed discrete-continuous nonlinear multi-objective optimisation problem. This work also proposes a dominance-based multi-objective differential evolution algorithm called Multi-Objective Specialist Population-based Differential Evolution (MOSPDE), which endows specialised subpopulations to exploit different regions of the Pareto front to favour the search for design trade-offs with a suitable generation of the gait and force transmission during the stance phase. The comparative study includes algorithms reported in the specialised literature based on dominance (MOPSO, MODE variants and NSGA-II), decomposition (MOEA/D-DE) and metric-driven (SMS-EMOA). The statistical analysis reveals that using the Pareto dominance search approach based on differential evolution and the inclusion of exhaustive exploitation can promote the reconfigurability of the bipedal locomotion mechanism with better trade-offs that satisfy the conflicting design objectives.

Multi‐objective design of energy harvesting enabled wireless networks based on evolutionary genetic optimisation

Sustainable operation of energy-restrained wireless network services requires multiple objectives to be satisfied synchronously. Among these objectives, reduced spectrum outage, energy conservation, and minimal packet transmission failures considerably affect the energy harvesting operation of these networks. These three objectives are associated with disparate protocol layers incorporating the transport, medium access control, and physical layers of traditional networking architecture. The authors investigate energy harvesting wireless communications by formulating the multi-objective optimisation problem comprising these global networking criteria, which are simultaneously optimised with the heuristic design procedure. For this, they employ a Pareto-based evolutionary genetic algorithm technique built in the wireless network design and operation to find the optimal set of all non-dominated solutions traversing the entire design search space. Besides, iterative implementation of the presented genetic optimisation model with distinct feasible integrations of crossover and mutation operations is performed to evaluate the proficiency of the proposed scheme for evaluating the Pareto-optimal frontier set. The influence of different combinations of these operations is examined and adaptively applied with appropriate genetic parameters tuning for efficient meta-heuristic search through the candidate solution space. Simulation results demonstrate that the proposed hybrid genetic mechanism outperforms the existing methods in terms of throughput, energy efficiency, and loss rate.

A modified feature selection method based on metaheuristic algorithms for speech emotion recognition

Feature selection plays an important role to build a successful speech emotion recognition system. In this paper, a feature selection approach which modifies the initial population generation stage of metaheuristic search algorithms, is proposed. The approach is evaluated on two metaheuristic search algorithms, a nondominated sorting genetic algorithm-II (NSGA-II) and Cuckoo Search in the context of speech emotion recognition using Berlin emotional speech database (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) database. Results show that the presented feature selection algorithms reduce the number of features significantly and are still effective for emotion classification from speech. Specifically, in speaker-dependent experiments of the EMO-DB, recognition rates of 87.66% and 87.20% are obtained using selected features by modified Cuckoo Search and NSGA-II respectively, whereas, for the IEMOCAP database, the accuracies of 69.30% and 68.32% are obtained using SVM classifier. For the speaker-independent experiments, we achieved comparable results for both databases. Specifically, recognition rates of 76.80% and 76.82% for EMO-DB and 59.37% and 59.52% for IEMOCAP using modified NSGA-II and Cuckoo Search respectively.

A novel method of S-box design based on discrete chaotic maps and cuckoo search algorithm

Substitution-boxes (S-boxes) are unique nonlinear elements, which are used to achieve the property of confusion in modern symmetric ciphers and offer resistance to cryptanalysis. The construction of strong S-boxes has gained considerable attention in the area of cryptography. In fact, the security of transmitted data is highly dependent on the strength of the S-boxes for the prevention of unauthorised access. Therefore, the creation of strong S-box with high nonlinearity score has been considered a significant challenge. This study presented a novel method for the designing of 8 × 8 S-boxes with selected cryptographic characteristics based on a cuckoo search (CS) algorithm and discrete-space chaotic map. Notably, the advantage of the proposed approach is indicated through the efficient randomisation and lower adjustable parameters in CS compared to GA and PSO. Also, this approach utilised a 1D discrete-space chaotic map with virtually unlimited key space to design initial S-boxes, which is another advantage over the methods based on continuous-space chaotic maps, which consist of the limited key space. Moreover, chaotic maps have a potential to overcome the trapping problem of a standard CS in the local optima, and they were used to generate initial S-boxes to achieve the desired quality and facilitate the metaheuristic search. Accordingly, the metaheuristic CS was used to find a notable S-box configuration which fulfilled the established criteria. This objective was achieved by searching for the optimal or near-optimal features which maximised the given fitness function. The performance of the proposed method was evaluated based on the established performance evaluation criteria, including bijectivity, nonlinearity, strict avalanche criteria, bit independence criteria, differential uniformity, and linear probability. Based on the results of proposed method performance was benchmarked against the results of the recently developed S-boxes, it was indicated that the S-boxes exhibited good cryptographic features and could resist various cryptanalysis attacks.

Kriging-assisted teaching-learning-based optimization (KTLBO) to solve computationally expensive constrained problems

In this paper, a novel algorithm KTLBO is presented to achieve computationally expensive constrained optimization. In KTLBO, Kriging is adopted to develop dynamically updated surrogate models for costly objective and inequality constraints. A data managing method aiming at solving expensive constrained problems is developed to archive, classify and update expensive samples, where a penalty function is set to adaptively select elite individuals. Moreover, based on the Teaching-Learning-based Optimization (TLBO), a Kriging-assisted two-phase optimization framework is presented to alternately conduct local and global searches. In Kriging-assisted Teaching and Learning Phases, two different prescreening operators considering the probability of feasibility are respectively proposed to select the high-quality samples around the present best solution and the samples exhibiting better space-filling performance, as an attempt to balance exploitation of surrogates and exploration of unknown area. In brief, KTLBO retains the meta-heuristic search mechanism of TLBO while adopting Kriging to accelerate its search, thereby acting as a novel idea for surrogate-assisted constrained optimization. Lastly, KTLBO is compared with 6 well-known methods on 27 benchmark cases, and then its significant advantages in expensive constrained optimization are verified. Furthermore, KTLBO is adopted to design the structure of a Blended-Wing-Body underwater glider, and the satisfactory solution is yielded.

An Enhanced Symbiotic Organism Search Algorithm (ESOS) for the Sizing Design of Pin Connected Structures

The symbiotic organism search (SOS) algorithm is a strong metaheuristic search engine with a great explorative capability which enables it to search for global optima efficiently. Nevertheless, its exploitive ability is less profound leading to a relatively low rate of convergence. In the current paper, an enhanced symbiotic organism search (ESOS) algorithm with the aim of improving the exploitive capability of the SOS algorithm is presented. The enhancements are done in the commensalism and mutualism phases of the algorithm. In the current study, a multi-ecosystem mechanism is also employed. The presented ESOS algorithm is used along with two different constraint handling methods, namely mapping strategy (MS) and penalty function (PF). This leads to two variants of the ESOS, termed as ESOS-MS and ESOS-PF. The two presented ESOS variants are utilized to optimize the weight of four benchmark truss structures. The results are compared with the optimal designs reported in the literature. The results demonstrate that the proposed ESOS variants not only can find better solutions compared to the other algorithms, but also are faster in doing so. Moreover, of the two presented ESOS variants, the ESOS-MS variant performs much better than the ESOS-PF variant.

Solution Merging in Matheuristics for Resource Constrained Job Scheduling

Matheuristics have been gaining in popularity for solving combinatorial optimisation problems in recent years. This new class of hybrid method combines elements of both mathematical programming for intensification and metaheuristic searches for diversification. A recent approach in this direction has been to build a neighbourhood for integer programs by merging information from several heuristic solutions, namely construct, solve, merge and adapt (CMSA). In this study, we investigate this method alongside a closely related novel approach—merge search (MS). Both methods rely on a population of solutions, and for the purposes of this study, we examine two options: (a) a constructive heuristic and (b) ant colony optimisation (ACO); that is, a method based on learning. These methods are also implemented in a parallel framework using multi-core shared memory, which leads to improving the overall efficiency. Using a resource constrained job scheduling problem as a test case, different aspects of the algorithms are investigated. We find that both methods, using ACO, are competitive with current state-of-the-art methods, outperforming them for a range of problems. Regarding MS and CMSA, the former seems more effective on medium-sized problems, whereas the latter performs better on large problems.

Feature grouping and selection: A graph-based approach

Most current feature selection techniques are focused on the incremental inclusion or exclusion of single individual features with respect to the candidate feature subset(s). The use of such approaches, where only the individual inclusion/exclusion of features is considered, means that information such as the collaborative contribution or correlation between features may be lost. The result is that the final selected feature subset may contain high levels of inter-feature redundancy, assuming that the key information embedded in the original feature set can still be retained. To address this problem, a general framework based on graph processing and three-way mutual information metrics is proposed in this paper that works by clustering similar features into groups, from which representative features are then drawn. Two different feature selection techniques based on this framework are presented: one by straightforward selection of representative features from the resulting feature groups and the other via a music-inspired metaheuristic search. Comparative experimental evaluation against traditional feature selection techniques over a diverse range of 20 benchmark datasets demonstrates the efficacy of the proposed approach. With these implementations, significant performance gains can be made in terms of classification accuracy in general and dimensionality reduction in particular while retaining feature semantics and considerably lessening the redundancy in the returned feature subsets.