Hybrid Evolutionary Algorithm Research Articles

Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems

In this paper, an improved hybridization of an evolutionary algorithm, named permutated oppositional differential evolution sine cosine algorithm (PODESCA) and also a sensitivity-based decision-making technique (SBDMT) are proposed to tackle the optimal planning of shunt capacitors (OPSC) problem in different-scale radial distribution systems (RDSs). The evolved PODESCA uniquely utilizes the mechanisms of differential evolution (DE) and an enhanced sine–cosine algorithm (SCA) to constitute the algorithm’s main structure. In addition, quasi-oppositional technique (QOT) is applied at the initialization stage to generate the initial population, and also inside the main loop. PODESCA is implemented to solve the OPSC problem, where the objective is to minimize the system’s total cost with the presence of capacitors subject to different operational constraints. Moreover, SBDMT is developed by using a multi-criteria decision-making (MCDM) approach; namely the technique for the order of preference by similarity to ideal solution (TOPSIS). By applying this approach, four sensitivity-based indices (SBIs) are set as inputs of TOPSIS, whereas the output is the highest potential buses for SC placement. Consequently, the OPSC problem’s search space is extensively and effectively reduced. Hence, based on the reduced search space, PODESCA is reimplemented on the OPSC problem, and the obtained results with and without reducing the search space by the proposed SBDMT are then compared. For further validation of the proposed methods, three RDSs are used, and then the results are compared with different methods from the literature. The performed comparisons demonstrate that the proposed methods overcome several previous methods and they are recommended as effective and robust techniques for solving the OPSC problem.

Computational optimization for porosity-dependent isogeometric analysis of functionally graded sandwich nanoplates

A simply and effectively computational optimization for porosity-dependent isogeometric analysis of functionally graded (FG) sandwich nanoplates is proposed for the first time. Porosity-dependent material properties are defined via the modified power law function. The distribution of ceramic volume fraction is approximated by using the multi-patch B-spline basis functions through the thickness direction. This approach ensures smoothly and continuously vary material properties across each layer, and automatically satisfies the C0-continuity at each layer interfaces. To consider length scale effects, the Eringen’s nonlocal elasticity theory is used to model porous FG sandwich nanoplates. Based on a combination of NURBS formulations and four variables refined plate theory, governing equations of the nanoplates are derived and employed to obtain natural frequencies of the porous FG sandwich nanoplates. The present approximation is easy to satisfy the requirement of at least third order derivatives of basis functions in approximate formulations of nanoplates. To save computational costs, an adaptive hybrid evolutionary firefly algorithm is used. Continuous design variables including the thickness of each layer and the ceramic volume fraction at control points are considered for constraint optimization problems. New results are performed and considered as benchmark results for further studies on the porous FG sandwich nanoplates.

Solving Multi-Objective Master Production Scheduling Model of Kalak Refinery System Using Hybrid Evolutionary Imperialist Competitive Algorithm

The improvement of operational planning in the field of oil refinery management is becoming increasingly essential and valid. The influential primary factor, among others, is the ever-changing economic climate. The industry must continually assess the potential impacts of variations in the final product demand, price fluctuations, crude oil compositions and even seek out immediate opportunities within the market. The Master Production Schedule (MPS) is a planned process within the Production Management System that provides a mechanism for active collaboration between the marketing and manufacturing processes. However, the problem of MPS is a predictable non-deterministic, polynomial-time and NP-hard combination optimisation issue. The global search for the best solution to the MPS problem involves determination and funds that many industries are reluctant to provide. Hence, the alternative approach using meta-heuristics could provide desirable and workable answers in a realistic computing period. In this paper, a unique hybrid Multi-Objective Evolutionary Imperialist Competitive Algorithm (MOEICA) is proposed. The algorithm combines the advantages of an Imperialist Competitive Algorithm (ICA) and a Genetic Algorithm (GA) to optimise a multi-objective master production schedule (MOMPS). The primary objective is to integrate the ICA with GA operators. The paper will also apply the optimised MOMPS to the Kalak Refinery System (KRS) operations using the proposed algorithm. The application involves determining the available capacity of each production line by estimating the parametric values for all failures. In addition, the gross requirements using demand forecasting and neural networks are defined. The proposed algorithm proved efficient in resolving the issues of the MOMPS model within KRS compared to the NSGAII and MOPSO algorithms. The results reflect that the novel MOEICA algorithm outperformed NSGAII and MOPSO in almost all measurements.

Hybrid evolutionary multi-objective optimisation using outranking-based ordinal classification methods

Abstract A large number of real-world problems require optimising several objective functions at the same time, which are generally in conflict. Many of these problems have been addressed through multi-objective evolutionary algorithms. In this paper, we propose a new hybrid evolutionary algorithm whose main feature is the incorporation of the Decision Maker’s (DM’s) preferences through multi-criteria ordinal classification methods in early stages of the optimisation process, being progressively updated. This increases the selective pressure towards the privileged zone of the Pareto front more in agreement with the DM’s preferences. An extensive experimental research was conducted to answer three main questions: i) to what extent the proposal improves the convergence towards the region of interest for the DM; ii) to what extent the proposal becomes more relevant as the number of objectives increases, and iii) to what extent the effectiveness of the hybrid algorithm depends on the particular multi-criteria method used to assign solutions to ordered classes. The issues used to evaluate our proposal and answer the questions were seven scalable test problems from the DTLZ test suite and some instances of project portfolio optimisation problems, with three and eight objectives. Compared to MOEA/D and MOEA/D-DE, the results showed that the proposed strategy obtains a better convergence towards the region of interest for the DM and also performs better characterisation of that zone on a wide range of objective functions.

Robust multi‐objective vehicle routing problem with time windows for hazardous materials transportation

This work focuses on a hazardous material (HazMat) vehicle routing problem with time windows (VRPTW). Given the multi-objective and uncertainty natures of HazMat transportation, a multi-objective robust VRPTW (MO-RVRPTW) model is proposed, which simultaneously optimise both the number of vehicles and the uncertain transportation risk. An uncertain set containing 32 potential incident scenarios is constructed to model the uncertain parameter. To handle the uncertain multi-objective problem (MOP), this work develops two versions of robust criterion to transform the MO-RVRPTW to its robust counterpart. A hybrid evolutionary algorithm (HEA) is designed to solve the robust counterpart, which integrates a push forward insertion heuristic for initial population construction, a route exchange crossover operator and a multi-component mutation (MCM) operator for generating the better offspring. The MCM is based on three basic local search operators and employs a sequential-move mechanism to improve the effectiveness of the algorithm. The proposed algorithm is tested on classical Solomon instances. Experiment results show that HEA is competitive in terms of convergence and diversity. A deterministic case is employed to justify the proposed robust criterion. In most cases, they can provide a set of robust non-dominated solutions with respect to both pareto optimality and robust optimality.

Privacy-aware cloud service composition based on QoS optimization in Internet of Things

With the advent of Internet of Things (IoT), cloud service composition problem has gotten more complex along with the composition of large number of cloud services and numerous complex requirements from the users as an NP-Hard problem. In order to compose various cloud services in the IoT context, a conceptual model is required besides an effective selection and composition model. Moreover, as an important aspect of today’s human life necessity, privacy preserving during sharing the data is a crucial concern. Therefore, privacy-aware cloud service composition along with improving the other quality of service (QoS) factors is an essential research motivation especially in the cloud based-IoT environments. In this paper, a privacy-aware cloud service composition approach with respect to QoS optimization in the IoT environment is proposed through presenting an IoT-based cloud service composition conceptual model regarding the privacy level computing model and a novel hybrid evolutionary algorithm using shuffled frog leaping algorithm (SFLA) and genetic algorithm (GA) which is called SFLA-GA. The proposed algorithm is applied to optimize the suggested service composition in terms of aggregation of different QoS factors as fitness value. Also, in order to help the users to select the proper composite service, they are categorized regarding their privacy preserving level which is obtained through a computational model. Simulation results revealed that the proposed approach improves the fitness in comparison to other contemporary algorithms.

Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility

Forests are important dynamic systems which are widely affected by fire worldwide. Due to the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threat. Three fuzzy-metaheuristic ensembles, based on adaptive neuro-fuzzy inference systems (ANFIS) incorporated with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) evolutionary algorithms are used to produce the forest fire susceptibility map (FFSM) of a fire-prone region in Iran. A sensitivity analysis is also executed to evaluate the effectiveness of the proposed ensembles in terms of time and complexity. The results revealed that all models produce FFSMs with acceptable accuracy. However, the superiority of the GA-ANFIS was shown in both recognizing the pattern (AUROCtrain = 0.912 and Error = 0.1277) and predicting unseen fire events (AUROCtest = 0.850 and Error = 0.1638). The optimized structures of the proposed GA-ANFIS and PSO-ANFIS ensembles could be good alternatives to traditional forest fire predictive models, and their FFSMs can be promisingly used for future planning and decision making in the proposed area.

Incorporating evolutionary computation for securing wireless network against cyberthreats

Due to the rapid growth of internet services, the demand for protection and security of the network against sophisticated attacks is continuously increasing. Nowadays, in network security, an intrusion detection system (IDS) plays an important role to detect intrusive activity. With the purpose of reducing the search dimensionality and enhancing classification performance of IDS model, in the literature several hybrid evolutionary algorithms have been investigated to tackle anomaly detection problems, but they have few drawbacks such as poor diversity, massive false negative rate, and stagnation. To resolve these limitations, in this study, we introduce a new hybrid evolutionary algorithm combining the techniques of grasshopper optimization algorithm (GOA) and simulated annealing (SA), called GOSA for IDS that extracts the most noteworthy features and eliminates irrelevant ones from the original IDS datasets. In the proposed method, SA is integrated into GOA, while utilizing it to increase the solution quality after each iteration of GOA. Support vector machine is used as a fitness function in the proposed method to select relevant features which can help to classify attacks accurately. The performance of the proposed method is evaluated on two IDS datasets such as NSL-KDD and UNSW-NB15. From experimental results, we observe that the proposed method outperforms existing state-of-the-art methods and attains high detection rate as 99.86%, an accuracy as 99.89%, and low false alarm rate as 0.009 in NSL-KDD and high detection rate as 98.85%, an accuracy as 98.96%, and low false alarm rate as 0.084 in UNSW-NB15.

A hybrid evolutionary algorithm for finding low conductance of large graphs

Given an undirected graph G=(V,E) with vertex set V and edge set E, the minimum conductance graph partitioning problem is to partition V into two subsets s=(A,Ā) such that its quotient cut is minimized. This problem arises in a number of significant real-world applications; however, it is known to be NP-hard and thus computationally challenging. We present in this work a hybrid evolutionary algorithm for finding low conductance of large graphs. To ensure a high search efficiency and efficacy, the algorithm relies on the general memetic search framework and integrates specialized search components (e.g., fast local optimization with a progressive neighborhood and quality-and-diversity based pool management). Computational experiments on a set of 60 large-scale real-world benchmark instances in the literature with up to 23 million vertices lead to the following main findings. First, the algorithm competes very favorably with the state-of-the-art methods and is able to find efficiently solutions of low conductance for the tested instances. Second, the dedicated search components are essential to ensure the high performance of the algorithm. Third, the algorithm can further ameliorate the solutions obtained by the well-known max-flow quotient-cut improvement algorithm. Finally, we illustrate an application of using the algorithm to detect meaningful community structures in complex networks.

Hybrid Particle Swarm Optimization with Sine Cosine Algorithm and Nelder–Mead Simplex for Solving Engineering Design Problems

This paper introduces a novel hybrid evolutionary algorithm that combines particle swarm optimization (PSO) algorithm with sine–cosine algorithm (SCA) and Nelder–Mead simplex (NMS) optimization technique. However, the algorithm of PSO has some drawbacks like locating local minima rather than global minima, low converge rate and low balance between exploration and exploitation. In this paper, the combination of PSO algorithm with update positions mathematical equation in SCA and NMS technique is presented in order to solve these problems. So a new hybrid strategy called PSOSCANMS is introduced. The SCA algorithm is based on the behavior of sine and cosine functions in the mathematical formula used for solutions. However, the NMS mathematical formulations attempt to replace the worst vertex with a new point, which depends on the worst point and the center of the best vertices. The combined effect of both mathematical formulations of PSO ensures a consistency of exploitation and exploration that makes the search in the search space more effective. Further, it escapes into the local minimum issue and resolves the low converge rate problem. In order to test PSOSCANMS’s performance, a set of 23 well-known unimodal and multimodal functions have been benchmarked. Experimental results showed that PSOSCANMS is more successful than PSO and outperforms the other state-of-the-art compared algorithms over the tested optimization problems. Moreover, an engineering design problem such as spring compression, welded beam is also considered. The result of the problems in engineering design and application problems shows that the algorithm proposed is relevant in difficult cases involving unknown search areas.

Learning ensembles of priority rules for online scheduling by hybrid evolutionary algorithms

This paper studies the computation of ensembles of priority rules for the One Machine Scheduling Problem with variable capacity and total tardiness minimization. Concretely, we address the problem of building optimal ensembles of priority rules, starting from a pool of rules evolved by a Genetic Programming approach. Building on earlier work, we propose a number of new algorithms. These include an iterated greedy search method, a local search algorithm and a memetic algorithm. Experimental results show the potential of the proposed approaches.

Hybrid Evolutionary Algorithm Using Optimal Placement of FACTS Devices for Total Transfer Capability

Hybrid Evolutionary Algorithm Using Optimal Placement of FACTS Devices for Total Transfer Capability

Improving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms

One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorithm is proposed which uses support vector machine classifier and cuckoo search algorithm in combination with the genetic algorithm in order to select the optimal features. In the proposed method, at first, a set of characteristics based on the Cepstral, Spectral and Fourier coefficients of the speech signal is extracted and then with the proposed hybrid algorithm, the operation of selecting the optimal feature set is performed. The results of the experiments on the famous Berlin's emotional speech database showed that using this proposed method for selecting the features, increases the classification accuracy to about 93%.

Clustering Nodes and Discretizing Movement to Increase the Effectiveness of HEFA for a CVRP

A Capacitated Vehicle Routing Problem (CVRP) is an important problem in transportation and industry. It is challenging to be solved using some optimization algorithms. Unfortunately, it is not easy to achieve a global optimum solution. Hence, many researchers use a combination of two or more optimization algorithms, which based on swarm intelligence methods, to overcome the drawbacks of the single algorithm. In this research, a CVRP optimization model, which contains two main processes of clustering and optimization, based on a discrete hybrid evolutionary firefly algorithm (DHEFA), is proposed. Some evaluations on three CVRP cases show that DHEFA produces an averaged effectiveness of 91.74%, which is much more effective than the original FA that gives mean effectiveness of 87.95%. This result shows that clustering nodes into several clusters effectively reduces the problem space, and the DHEFA quickly searches the optimum solution in those partial spaces.

A Two-Stage Heuristic Hybrid Evolutionary Algorithm for Virtual Machine Migration and Placement in Cloud Bursting

The platform for cloud computing offers virtualization and a dynamic pool of resources to the consumers of the cloud. The acceptance and demand of cloud is growing on a regular basis. Cloud computing offers utility-based consumer services across the globe on a pay as you go strategy. Live Virtual Machine (VM) migration sets the basic foundation for cloud management. It hasakey role in reducing operating expenditure and revising quality of service without disruption of cloud services running in the VM. A lot of research has been done to yield better performance in live VM migration and has seen noteworthy development and accomplishment. However, some crucial problems require results and enhancement. With the growth of new cloud computing models like Mobile Edge Computing, certain problems related to optimization need to be addressed. The primary aim of this research work is to emphasize on optimum functioning of live migration. A migration algorithm to consolidate the computational resources, storage resources and network resources dynamically with a two-stage heuristic hybrid evolutionary algorithm is discussed. The resources are consolidated to cut down the energy and cost utilization depending upon the evolutionary Particle Swarm Optimization and the Ant Colony Optimization algorithms. These algorithms can rapidly identify the migrating virtual machines and locate their positions respectively.

Hybrid Evolutionary Design Space Exploration Algorithm With Defence Against Third Party IP Vulnerabilities

With the globalization of the semiconductor industry, the supply chain is fragmented across geographies, which has resulted in the importing of third-party IP cores in designs. Such third party IP cores have increased concerns about hardware Trojans. To address such concerns, development of an effective Trojan detection methodology is crucial. In this article, we present a low-cost Trojan detection and activation prevention mechanism for high level synthesis. In particular, a particle swarm optimization-based design space exploration method is proposed which is combined with a Trojan detection mechanism to minimize resource requirements. Additionally, a hybrid evolutionary algorithm-based Trojan detection method is formed with a novel vendor allocation procedure to find low-cost solutions. The proposed algorithm is compared with existing methods-based on Pareto and non-Pareto-based evaluations to verify their efficiency. From the experiment results, it has been verified that the proposed algorithm offers defence against Trojans similar to existing algorithms with less hardware overhead.

A New Hybrid Evolutionary Algorithm for the Treatment of Equality Constrained MOPs

Multi-objective evolutionary algorithms are widely used by researchers and practitioners to solve multi-objective optimization problems (MOPs), since they require minimal assumptions and are capable of computing a finite size approximation of the entire solution set in one run of the algorithm. So far, however, the adequate treatment of equality constraints has played a minor role. Equality constraints are particular since they typically reduce the dimension of the search space, which causes problems for stochastic search algorithms such as evolutionary strategies. In this paper, we show that multi-objective evolutionary algorithms hybridized with continuation-like techniques lead to fast and reliable numerical solvers. For this, we first propose three new problems with different characteristics that are indeed hard to solve by evolutionary algorithms. Next, we develop a variant of NSGA-II with a continuation method. We present numerical results on several equality-constrained MOPs to show that the resulting method is highly competitive to state-of-the-art evolutionary algorithms.

Optimal design of FG sandwich nanoplates using size-dependent isogeometric analysis

In this study, an effectively computational optimization approach for size-dependent isogeometric analysis of functionally graded (FG) sandwich nanoplates is proposed. The multi-patch B-spline basis functions through the thickness direction are employed to approximate distributions of ceramic volume fraction. This approach smoothly and continuously ensures the distributions of ceramic volume fraction across each layer, and automatically satisfies the C0-continuity at each layer interface. The material properties based on both Mori–Tanaka scheme and the rule of mixture are implemented. To capture the size effects, the Eringen's nonlocal elasticity is used. Governing equations are then derived and a combination of the NURBS formulation and four variables refined plate theory (RPT) are employed to analyze natural frequencies of the FG sandwich nanoplates. Thanks to continuous higher-order derivatives of NURBS basis functions, the present approximation is easy to satisfy the requirement of C2-continuity of the FG sandwich nanoplates. An adaptive hybrid evolutionary firefly algorithm with fast convergence speed and less computational cost is used as an optimizer. The ceramic volume fraction across the z-direction at control points and the thickness of each layer are considered as continuous design variables. Numerical examples for free vibration and optimization analyses of the FG sandwich nanoplates are examined. Several new results are obtained and considered as benchmark problems for further studies on the FG sandwich nanoplates.

Hybrid evolutionary algorithm (NNACOR) for energy minimization in a wireless mesh topology towards green computing

Wireless sensor networks are a very rapidly emerging class of network structures being utilized in almost all computing environments. Energy consumption is an essential criterion especially with the recent need in conservation of energy and carbon footprint. The proposed research paper investigated the prospects of green computing towards implementation of a hybrid algorithm (NNACOR) based on evolutionary computing model deriving its behaviour from naturally occurring phenomenon for reducing the energy consumed by each node in the mesh topology. The experiments have been conducted in NS2 and compared against the conventional AODC routing protocol, and the observed results indicate a superior performance of the proposed evolutionary model by providing up to a 48% in energy saving as against a 44% energy saving reported in the literature.

Knowledge-based multi-objective genetic algorithms for the design of water distribution networks

Abstract Water system design problems are complex and difficult to optimise. It has been demonstrated that involving engineering expertise is required to tackle real-world problems. This paper presents two engineering inspired hybrid evolutionary algorithms (EAs) for the multi-objective design of water distribution networks. The heuristics are developed from traditional design approaches of practicing engineers and integrated into the mutation operator of a multi-objective EA. The first engineering inspired heuristic is designed to identify hydraulic bottlenecks within the network and eliminate them with a view to speeding up the algorithm's search to the feasible solution space. The second heuristic is based on the notion that pipe diameters smoothly transition from large, at the source, to small at the extremities of the network. The performance of the engineering inspired hybrid EAs is compared with Non-Dominated Sorting Genetic Algorithm II and assessed on three networks of varying complexity, two benchmarks and one real-world network. The experiments presented in this paper demonstrate that the incorporation of engineering expertise can improve EA performance, often producing superior solutions both in terms of mathematical optimality and also engineering feasibility.