Efficient Metaheuristic Algorithm Research Articles

Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference

Microservices is a new paradigm in cloud computing that separates traditional monolithic applications into groups of services. These individual services may correlate or cross multi-clouds. Compared to a monolithic architecture, microservices are faster to develop, easier to deploy, and maintain by leveraging modern containers or other lightweight virtualization. To satisfy the requirements of end-users and preferences, appropriate microservices must be selected to compose complicated workflows or processes from within a large space of candidate services. The microservice composition should consider several factors, such as user preference, correlation effects, and fuzziness. Due to this problem being NP-hard, an efficient metaheuristic algorithm to solve large-scale microservice compositions is essential. We describe a microservice composition problem for multi-cloud environments that considers service grouping relations and corresponding correlation effects of the service providers within intra- or inter-clouds. We use the triangular fuzzy number to describe the uncertainty of QoS attributes, the improved fuzzy analytic hierarchy process to calculate multi-attribute QoS, construct fuzzy weights related to user preferences, and transform the multi-optimal problem into a single-optimal problem. We propose a new artificial immune algorithm based on the immune memory clone and clone selection algorithms. We also introduce several optimal strategies and conduct numerical experiments to verify effects and efficiencies. Our proposed method combines the advantages of monoclone, multi-clone, and co-evolution, which are suitable for the large-scale problems addressed in this paper.

Beetle antennae search without parameter tuning (BAS-WPT) for multi-objective optimization

Beetle antennae search (BAS) is an efficient meta-heuristic algorithm inspired by foraging behaviors of beetles. This algorithm includes several parameters for tuning and the existing results are limited to solve single objective optimization. This work pushes forward the research on BAS by providing one variant that releases the tuning parameters and is able to handle multi-objective optimization. This new approach applies normalization to simplify the original algorithm and uses a penalty function to exploit infeasible solutions with low constraint violation to solve the constraint optimization problem. Extensive experimental studies are carried out and the results reveal efficacy of the proposed approach to constraint handling.

An Efficient VNS Algorithm to Solve the Multi-Attribute Technician Routing and Scheduling Problem

This article addresses a technician routing and scheduling problem inspired from an application for the repair of electronic transactions equipment. It consists of designing routes for staff to perform requests while considering certain constraints and resources. The objective is to minimize a linear combination of total weighted distance, overtime, and maximize the served requests. An efficient meta-heuristic algorithm based on variable neighborhood search with an adaptive memory and advanced diversity management method is proposed. Numerical results show that the meta-heuristic outperforms the best existing algorithm from the literature which is a Tabu Search.

A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons

The Sine Cosine Algorithm (SCA) is a recently developed efficient metaheuristic algorithm to find the solution of global optimization problems. However, in some circumstances, this algorithm suffers the problem of low exploitation, skipping of true solutions and insufficient balance between exploration and exploitation. Therefore, the present paper aims to alleviate these issues from SCA by proposing an improved variant of SCA called HSCA. The HSCA modifies the search mechanism of classical SCA by including the leading guidance and hybridizing with simulated quenching algorithm. The proposed HSCA is tested on classical benchmark set, standard and complex benchmarks sets IEEE CEC 2014 and CEC 2017 and four engineering optimization problems. In addition to these problems, the HSCA is also used to train multilayer perceptrons as a real-life application. The experimental results and analysis on benchmark problems and real-life application problems demonstrate the superiority of the HSCA as compared to other comparative optimization algorithms.

An Efficient JAYA Algorithm with Lévy Flight for Non-linear Channel Equalization

Neural network (NN) based equalizers are known to outperform the linear equalizers based on finite impulse response (FIR) adaptive filter for highly dispersive and non-linear channels. To overcome the limitations of the back-propagation (BP) algorithm, metaheuristic algorithms are emerging as promising alternatives for training the NN based equalizers. JAYA is a simple and efficient metaheuristic algorithm. Hence, its application to channel equalization problem is worth investigating. Despite its simplicity and efficiency, the JAYA algorithm has problems such as being trapped in local minima due to insufficient diversity of population and weak exploration capability. To alleviate these issues, in this paper the concept of Lévy flight (LF) and greedy selection scheme has been incorporated into the basic JAYA algorithm. The LF concept enhances the population diversity and thus avoids the state of stagnation. The greedy selection scheme is employed to improve the exploitation ability without loss of population diversity. Furthermore, in order to maintain the balance between the exploration and exploitation capabilities of the algorithm, an adaptive Lévy index is proposed based on a linear control parameter strategy. An extensive simulation-based sensitivity analysis of proposed method called JAYA algorithm with Lévy flight (JAYALF) with respect to key parameters is carried out to select the optimized values for these parameters. In order to validate the local optima avoidance ability, exploitation and convergence rate of the proposed JAYALF algorithm, it is tested on seventeen well-known unimodal and multimodal benchmark functions and to verify the effectiveness of the JAYALF for non-linear channel equalization problem, three wireless communication channels with two different nonlinearities have been considered for simulation. In addition, the non-parametric pairwise Wilcoxon rank-sum test has been employed to test the statistical validity of the results obtained from JAYALF. The results of experiments and statistical test demonstrate that the proposed algorithm significantly outperforms JAYA, variants of JAYA, state-of-the-art algorithms and BP algorithm in terms of solution quality, convergence speed, and robustness. Furthermore, the results of experimental analyses conducted indicate that proposed JAYALF algorithm has a better exploration ability and converges quickly without getting trapped into local optima.

A space transformational crow search algorithm for optimization problems

New and efficient meta-heuristic algorithms are always in demand to solve real world optimization problems due to its exploiting capability in the search domain to generate the global optimal solution. Crow search algorithm (CSA) is one of the latest meta-heuristic algorithms introduced in the literature to solve optimization tasks. The clever behaviour of crows attracted the researchers to think how to achieve a better optimization by using crow as a base element. Like other optimization algorithms, the CSA suffers with local optima and stagnation problem. In addition, for complex real world problems, CSA has not sufficient exploration capability. Therefore, in the current work, an attempt is made to enhance the explorative behaviour of the CSA by combining the space transform search (STS) method. The proposed algorithm is named as STS-CSA. The proposed STS-CSAintegrates space transformation search technique and computes the solution in current search space and transformed search space simultaneously to generate solutions that is closer to global optimum solution. To assess the performance in solving optimization problems, STS-CSA has been evaluated by applying standard IEEE CEC 2017 benchmark functions. Three real-world engineering problems are also verified to assess the effectiveness of the proposed algorithm in solving the practical problems. The performed analysis such as statistical measure, convergence analysis and complexity measure reveal that the proposed method is reliable and efficient in solving practical optimization problems.

Optimum design of reinforced concrete retaining walls with the flower pollination algorithm

The flower pollination algorithm (FPA) is an efficient metaheuristic optimization algorithm mimicking the pollination process of flowering species. In this study, FPA is applied, for the first time, to the optimum design of reinforced concrete (RC) cantilever retaining walls. It is found that FPA offers important savings with respect to conventional design approaches and that it outperforms genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm in this design problem. Furthermore, parameter tuning reveals that the best FPA performance is achieved for switch probability values ranging between 0.4 and 0.7, a population size of 20 individuals and a Levy flight step size scale factor of 0.5. Finally, parametric optimum designs show that the optimum cost of RC retaining walls increases rapidly with the wall height and smoothly with the magnitude of surcharge loading.

Integrating redundancy, diversity, and hardening to improve security of industrial internet of things

ABSTRACTAs the Industrial Internet of Things (IIoT) becomes more ubiquitous in critical application domains, such as smart water-distribution and transportation systems, providing security and resilience against cyber-attacks grows into an issue of utmost importance. Cyber-attacks against critical infrastructure pose significant threats to public health and safety. To alleviate the severity of these threats, various security techniques are available, including redundancy, diversity, and hardening. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach that integrates redundancy (deploying additional components and devices), diversity (using multiple implementation variants), and hardening (reinforcing individual components) techniques for designing secure and resilient IIoT systems. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design. We show that finding optimal designs is an NP-hard problem, and then present an efficient meta-heuristic algorithm that finds near optimal designs in practice. To demonstrate the applicability of our framework, we present two case studies in water-distribution and transportation systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risks at the same cost.

Dimension-by-dimension enhanced cuckoo search algorithm for global optimization

Cuckoo search (CS) algorithm is an efficient meta-heuristic algorithm that has been successfully applied in many fields. However, the algorithm uses the whole updating and evaluating strategy on solutions. For solving multi-dimensional optimization problems, solutions with partial dimension evolution may be discarded due to mutual interference among dimensions. Therefore, this strategy may deteriorate the quality solution and convergence rate of algorithm. To overcome this defect and enhance the algorithm performance, a dimension-by-dimension enhanced CS algorithm is proposed. In the global explorative random walk, the improved algorithm uses the dimension-by-dimension updating and evaluating strategy on solutions. This strategy combines the updated values of each dimension with the values of other dimensions into a new solution. In addition, a greedy strategy is adopted to accept new solution and the search center is set as the current optimal solution. The proposed algorithm was tested on fourteen well-known benchmark functions. The numerical results show that the improved algorithm can effectively enhance the quality solution and convergence rate for the global optimization problems.

An Ameliorative Hybrid Algorithm for Solving the Capacitated Vehicle Routing Problem

One of the most applicable versions of the Vehicle Routing Problem (VRP) which has been widely studied in logistic services is Capacitated Vehicle Routing Problem (CVRP). There are many algorithms to solve the CVRP to minimize total travelled distance. Some of the most recent and efficient metaheuristic algorithms are capable of generating solutions within 0.5% to 1% gap from the optimum for instance problems adopted from the literature considering hundreds or thousands of demand points. In this contribution, a novel hybrid algorithm is proposed based on Gravitational Emulation Local Search (GELS) and Genetic Algorithm (GA). This algorithm alleviates the weaknesses of the GELS algorithm. The performance of the proposed algorithm, which is called GELSGA, is compared with other meta-heuristics. The obtained results show that the proposed algorithm can compete vigorously with them. In addition, the proposed algorithm could obtain solutions close to the Best Known Solutions (BKS) for many instance problems.

An efficient meta-heuristic algorithm for solving capacitated vehicle routing problem

This work aims to develop an enhanced Perturbation based Variable Neighborhood Search with Adaptive Selection Mechanism (PVNS ASM) to solve the capacitated vehicle routing problem (CVRP). This approach combined Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on success rate of each perturbation scheme along the search. The ASM helped algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with existing heuristics in the literature has been performed on 21 CVRP benchmarks. The computational results proof that the developed method is competitive and very efficient in achieving high quality solution within reasonable computation time.

Seismic fragility assessment of optimally designed steel moment frames

This study is devoted to seismic performance assessment of optimally designed steel moment frames (SMFs) in the framework of performance-based design (PBD). The methodology presented in this work includes three phases. The first phase involves the optimization of SMFs by employing an efficient metaheuristic algorithm to meet the PBD requirements according to FEMA-350 code. Subsequently, the overall damage index (ODI) is calculated for the obtained optimal SMFs based on the Park-Ang local damage index (DIPA). In the second phase, incremental dynamic analysis (IDA) is conducted for the optimally designed SMFs and their fragility curves are derived and their collapse margin ratios (CMRs) are determined based on FEMA-P695. In the last phase, the fragility curves of the optimal SMFs are generated for different damage levels ranging from slight damage to collapse state and a new damage measure termed as damage margin ratio (DMR) is introduced to assess the damage-resistance capacity of the SMFs at the different damage levels. In order to illustrate the efficiency of the proposed methodology, three numerical examples of 3-, 6-, and 12-story SMFs are presented and the total cost of optimal SMFs, including initial and seismic damage costs, are determined. The numerical results demonstrate that the SMF with the best total cost has the best CMR, DMR, and degree of repairability.

A hybrid self-adaptive sine cosine algorithm with opposition based learning

Real-world optimization problems demand an efficient meta-heuristic algorithm which maintains the diversity of solutions and properly exploits the search space of the problem to find the global optimal solution. Sine Cosine Algorithm (SCA) is a recently developed population-based meta-heuristic algorithm for solving global optimization problems. SCA uses the characteristics of sine and cosine trigonometric functions to update the solutions. But, like other population-based optimization algorithms, SCA also suffers the problem of low diversity, stagnation in local optima and skipping of true solutions. Therefore, in the present work, an attempt has been made towards the eradication of these issues, by proposing a modified version of SCA. The proposed algorithm is named as modified Sine Cosine Algorithm (m-SCA). In m-SCA, the opposite population is generated using opposite numbers based on perturbation rate to jump out from the local optima. Secondly, in the search equations of SCA self-adaptive component is added to exploit all the promising search regions which are pre-visited. To evaluate the effectiveness in solving the global optimization problems, m-SCA has been tested on two sets of benchmark problems – classical set of 23 well-known benchmark problems and standard IEEE CEC 2014 benchmark test problems. In the paper, the performance of proposed algorithm m-SCA is also tested on five engineering optimization problems. The conducted statistical, convergence and average distance analysis demonstrate the efficacy of the proposed algorithm to determine the efficient solution of real-life global optimization problems.

Including preventive maintenance activities in an unrelated parallel machine environment with dependent setup times

Abstract The assumption that machines are always available during the production horizon may not be true in real industrial settings. One of the main causes of machine unavailability is the failure of equipment and an efficient way to reduce the failure frequency is through preventive maintenance activities. From that the convenience of including preventive maintenance into the production planning process. In this paper we study a new scheduling problem on unrelated parallel machines that considers, in conjunction, preventive maintenance activities and setup times depending on the sequence and the machine. We present a mathematical formulation for this problem and derive valid inequalities to improve its performance, allowing to the model to obtain optimal solutions for small-medium instances. In addition, we design an efficient metaheuristic algorithm based on a multi-start strategy for solving larger instances. We carry out an extensive computational experimentation to investigate the scope of the model, to validate the influence of the valid inequalities and to assess the performance of the proposed algorithm.

A novel stochastic fractal search algorithm for optimal allocation of distributed generators in radial distribution systems

This paper presents an application of a novel stochastic fractal search algorithm (SFSA) for solving the optimal allocation of distributed generators (OADG) problem in radial distribution systems (RDSs). The SFSA method is a newly efficient meta-heuristic algorithm inspired by the growing process of nature by using a mathematical idea known as fractal for solving optimization problems. The advantages of the SFSA method are simple implementation and few control parameters. The method is proposed to deal with the OADG problem by minimizing the active power loss, improving the voltage profile, and increasing the voltage stability while satisfying various constraints including active and reactive power balance, bus voltage limits, DG capacity limits, branch current limits, and DG penetration limit. The effectiveness of the proposed SFSA method has been verified on the IEEE 33-bus, 69-bus, and 118-bus RDSs and the obtained results have been validated via comparing to those from other methods in the literature. The result comparisons from the test systems have indicated that the proposed method can obtain higher quality solutions than many other methods for the considered scenarios from the test systems. Therefore, the proposed SFSA can be a very effective and favorable method for solving the OADG problem.

Developing the Inventory Routing Problem with Backhauls, Heterogeneous Fleet and Split Service

One of the most important points in a supply chain is customer-driven modeling, which reduces the bullwhip effect in the supply chain, as well as the costs of investment on the inventory and efficient transshipment of the products. Their homogeneity is reflected in the Inventory Routing Problem, which is a combination of distribution and inventory management. This paper expands the classical Inventory Routing Problem based on the Multiple Delivery Strategy along with one of the functionalities of routing problem, namely, backhauls, with a priority consideration for linehaul customers. Then it has been modeled in the form of a problem with Multi-period, multi-product, and multi- vehicle planning horizons in which stock out is not allowed. Moreover, for an optimal use of the vehicle capacity to serve the linehaul and backhaul customers, this study adopted a “split service” problem to the model, which also increases the complexity of the problem. First, considering the above-mentioned assumptions, a new mathematical model is proposed in the form of mixed integer programming for the problem defined in this paper. Then, since the stated problem can be considered among the non-deterministic polynomial-time hard, an efficient meta-heuristic genetic algorithm is provided for solving it. At the end, the numerical results obtained by this algorithm are analyzed using the randomized test problems. The result shows that by adopting a split service approach, 70% of the test problems will demonstrate cost reduction.

Kafes Sistemlerin BB-BC Algoritması ile Boyut Optimizasyonu Üzerine bir Çalışma: İnceleme ve Sayısal Deneyler

In the last decades, great attention has been paid to structural optimization with stochastic methods. The applications of metaheuristic algorithms have become popular, which mostly provide solutions with adequate precision for the structural optimization problems from an engineering point of view. However, these algorithms should be specifically tuned for the considered problem to obtain satisfactory results. Big Bang - Big Crunch is one of the efficient metaheuristic optimization algorithms that is based on the famous theory on the evolution of the universe. A considerable number of researchers presented applications of the Big Bang - Big Crunch algorithm for the size optimization of trusses, which is an inviting challenge in the structural optimization field. This study revisits the size optimization of trusses with continuous variables using the Big Bang - Big Crunch algorithm, discusses the previously introduced improvements and presents the results of a few experimental modifications.

Geometric structure information based multi-objective function to increase fuzzy clustering performance with artificial and real-life data

Grey wolf optimizer (GWO) is an efficient meta-heuristic algorithm that is inspired by the particular hunting behavior and leadership hierarchy of grey wolves in nature. In this paper, an efficient opposition-based grey wolf optimizer algorithm is proposed for solving the fuzzy clustering problem over artificial and real-life data. This work also tries to use the benefit of fuzzy properties which presents capability to handle overlapping clusters. However, centroid information and geometric structure information of clusters are the two important issues in fuzzy data clustering to improve the clustering performance. According to, in this paper, we derive two-objective functions, such as compactness and overlap–partition (OP) measures to handle above drawbacks. The centroid information issue is solved by compactness measure, and the OP measure is used to handle the geometric structure of clustering problem. Additionally, in the proposed clustering approach, the concept of opposition-based generation jumping and opposition-based population initialization is used with the standard GWO to enhance its computational speed and convergence profile. The efficiency of the proposed algorithm is shown for five artificial datasets and five real-life datasets of varying complexities. Experimental results show that the proposed method outperforms some existing methods with good clustering qualities.

Scatter search for the soft graph colouring problem

The soft graph colouring problem is a model that has been used to solve problems such as scheduling under uncertainty, assignment of frequencies in the radioelectric spectrum, and pattern recognition that in certain problems can deal with risky items susceptible of error I or error II. This problem is NP-hard and requires efficient metaheuristic algorithms in order to solve it. In this paper, a scatter search algorithm is proposed and developed. This algorithm gives the best solutions in the test instances used to this day.

An approximate/exact objective based search technique for solving general scheduling problems

In this paper, we analyze single machine scheduling problems under the following minimization objectives: the maximum completion time (makespan), the total completion time and the maximum lateness, including fundamental practical aspects, which often occur in industrial or manufacturing reality: release dates, due dates, setup times, precedence constraints, deterioration (aging) of machines, as well as maintenance activities. To solve the problems, we propose an efficient representation of a solution and a fast neighborhood search technique, which calculates an approximation of criterion values in a constant time per solution in a neighborhood. On this basis, a novel approximate/exact search technique, using exact as well as approximate criterion values during search process, is introduced and used to develop efficient metaheuristic algorithms dedicated to the considered problems. Their efficiency is verified during computational experiments.