A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads

Optimization methods are close to become a common task in the design process of many mechanical engineering fields, specially those related with the use of composite materials which offer the flexibility in the design of both the shape and the material properties and so, are very suitable to any optimization process. While nowadays there exist a large number of solution methods for optimization problems there is not much information about which method may be most reliable for a specific problem. Genetic algorithms have been presented as a family of methods which can handle most of engineering problems. However, starting from a common basic set of rules many algorithms which differ slightly from each other have been implemented even in commercial software packages. This work presents a comparative study of three common Genetic Algorithms: Archive-based Micro Genetic Algorithm (AMGA), Neighborhood Cultivation Genetic Algorithm (NCGA) and Non-dominate Sorting Genetic Algorithm II (NSGA-II) considering three different strategies for the initial population. Their performance in terms of solution, computational time and number of generations was compared. The benchmark problem was the optimization of a T-shaped stringer commonly used in CFRP stiffened panels. The objectives of the optimization were to minimize the mass and to maximize the critical buckling load. The comparative study reveals that NSGA-II and AMGA seem the most suitable algorithms for this kind of problem.     

SSABA: Search Step Adjustment Based Algorithm

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.     

Availability and cost-centered preventive maintenance scheduling of continuous operating series systems using multi-objective genetic algorithm: A case study

This article presents a multi-objective (maximization of availability and minimization of maintenance cost) preventive maintenance (PM) scheduling model for a continuous operating series system (COSS) which do not provide an off-working period for PM. The objective functions are optimized by using a Multi-Objective Genetic Algorithm (MOGA). The effectiveness of the model is demonstrated through a coal-fired boiler-tube. The case study shows that the model can improve the availability along with profound reduction of the maintenance cost, i.e., increases the profit of the plant.     

Multi-objective design optimisation of repairable k-out-of-n subsystems in series with redundant dependency

Several researches have been investigated on Multi-Objective Redundancy Allocation Problems (MORAPs), but none of them have considered the redundant dependency at the design stage. This latter which is a special kind of failure dependency can affect significantly the system performance. Due to this fact, this paper deals with the multi-objective system design optimisation with dependent components by focusing on two objectives: maximisation of system availability and minimisation of system cost with components choice and weight constraints. A system consisting of many k-out-of-n repairable subsystems connected in series is considered. The components of a subsystem are supposed to be identical and may be dependent. They are selected from a set of available component types. In addition to the redundancy level and the number of repair teams allocated to each subsystem, the choice of components type and the dependency level are also considered as decision variables. Since the described problem is NP hard, we propose three multi-objective meta-heuristic algorithms based on Non-dominated Sorting Genetic Algorithm (NSGA II) and Strength Pareto Evolutionary Algorithm (SPEA II) with different constraints handling. An exact method is also applied. To analyse their performances, numerical applications are provided and comparisons based on different well-known metrics are presented.     

Comparison of evolutionary computation algorithms for solving bi-objective task scheduling problem on heterogeneous distributed computing systems

The task scheduling problem in heterogeneous distributed computing systems is a multiobjective optimization problem (MOP). In heterogeneous distributed computing systems (HDCS), there is a possibility of processor and network failures and this affects the applications running on the HDCS. To reduce the impact of failures on an application running on HDCS, scheduling algorithms must be devised which minimize not only the schedule length (makespan) but also the failure probability of the application (reliability). These objectives are conflicting and it is not possible to minimize both objectives at the same time. Thus, it is needed to develop scheduling algorithms which account both for schedule length and the failure probability. Multiobjective Evolutionary Computation algorithms (MOEAs) are well-suited for Multiobjective task scheduling on heterogeneous environment. The two Multi-Objective Evolutionary Algorithms such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Evolutionary Programming (MOEP) with non-dominated sorting are developed and compared for the various random task graphs and also for a real-time numerical application graph. The metrics for evaluating the convergence and diversity of the obtained non-dominated solutions by the two algorithms are reported. The simulation results confirm that the proposed algorithms can be used for solving the task scheduling at reduced computational times compared to the weighted-sum based biobjective algorithm in the literature.     

Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE)

In this paper, we consider the problem of the optimization of the inspection intervals of the High Pressure Injection System (HPIS) of a Pressurized Water Reactor (PWR). For its solution, we investigate the use of Differential Evolution (DE) and compare it to another popular Evolutionary Algorithm (EA), the Genetic Algorithm (GA). In the comparison, we look in particular at the computation time and at the characteristics of the Pareto frontier. The problem is first treated as a single-objective optimization (SO) and then as a multi-objective optimization (MO). For this latter, a Multi-Objective Differential Evolution (MODE) code has been purposely developed, in Matlab.     

Comparison of multi-objective evolutionary approaches for task scheduling in distributed computing systems

Parallel and distributed systems play an important part in the improvement of high performance computing. In these type of systems task scheduling is a key issue in achieving high performance of the system. In general, task scheduling problems have been shown to be NP-hard. As deterministic techniques consume much time in solving the problem, several heuristic methods are attempted in obtaining optimal solutions. This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Non-dominated Sorting Particle Swarm Optimization Algorithm (NSPSO) to schedule independent tasks in a distributed system comprising of heterogeneous processors. The problem is formulated as a multi-objective optimization problem, aiming to obtain schedules achieving minimum makespan and flowtime. The applied algorithms generate Pareto set of global optimal solutions for the considered multi-objective scheduling problem. The algorithms are validated against a set of benchmark instances and the performance of the algorithms evaluated using standard metrics. Experimental results and performance measures infer that NSGA-II produces quality schedules compared to NSPSO.     

Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches

Achieving competitiveness in nowadays manufacturing market goes through being cost and time-efficient as well as environmentally harmless. Reconfigurable manufacturing system (RMS) is a paradigm that is able to meet these challenges due to its scalability and integrability. In this paper, we aim to solve the multi-objective sustainable process plan generation problem in a reconfigurable environment. In addition to the total production cost and the completion time, we use the amount of greenhouse gases (GHG) emitted during the manufacturing process as a sustainability criterion. We propose an iterative multi-objective integer linear programming (I-MOILP) approach and its comparison with adapted versions of the two well-known evolutionary algorithms, respectively, the Archived Multi-Objective Simulated Annealing (AMOSA) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Moreover, we study the influence of the probabilities of genetic operators on the convergence of the adapted NSGA-II. To illustrate the applicability of the three approaches, an example is presented and obtained numerical results analysed.     

Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm (WOA) to optimize the parameter weights of the Bidirectional Recurrent Neural Networks (BRNN). Furthermore, a time series dataset is tested in the experiments of e-commerce demand forecasting. Finally, the results were compared to many versions of the state-of-the-art optimization techniques such as the Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). A statistical analysis has proven that the proposed algorithm can work significantly better by statistical analysis test at the P-value less than 0.05 with a one-way analysis of variance (ANOVA) test applied to confirm the performance of the proposed ensemble model. The proposed Algorithm achieved a root mean square error of RMSE (0.0000359), Mean (0.00003593) and Standard Deviation (0.000002162).     

IMMUNE NETWORK SIMULATION WITH MULTIOBJECTIVE GENETIC ALGORITHMS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION

The paper presents a method called MOGA-INS for Multidisciplinary Design Optimization (MDO) of systems that involve multiple competing objectives with a mix of continuous and discrete variables. The method is based on the Immune Network Simulation ( INS) approach that has been extended by combining it with a Multi-Objective Genetic Algorithm ( MOGA). MOGA obtains Pareto solutions for multiple objective optimization problems in an all-at-once manner. INS provides a coordination strategy for subsystems in MDO to interact and is naturally suited for genetic algorithm-based optimization methods. The MOGA-INS method is demonstrated with a speed-reducer example, formulated as a two-level two-objective design optimization problem.     

Enhanced Heap-Based Optimizer Algorithm for Solving Team FormationProblem

Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO’s optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum.     

一种快速构造非支配集的方法--擂台法则

多目标进化算法是用来解决多目标优化问题的,为了提高多目标算法的效率,提出了一种快速构造非支配集的方法——擂台法则。它的时间耗费要低于Deb和Jensen提出的构造非支配集的方法。在实验中将擂台法则同Deb和Jensen的方法进行了比较,最后实验结果证明前者在运行时间上要优于后两者。     

基于组合优化策略的热封机构设计

目的通过组合优化策略对热封机构进行结构优化设计。方法建立热封机构有限元参数化模型和多目标优化模型,通过动力学有限元分析获得热封机构结构强度薄弱环节,并采用最优拉丁超立方实验设计对影响热封机构结构强度的主要参数进行研究,分析参数与应力位移之间的响应规律,构造基模型对热封机构进行基于组合优化策略的结构优化设计,取多岛遗传算法和序列二次规划法组合的优化策略逼近最优解。结果组合优化过程中的校正决定系数接近1,模型的结构应力最大值和位移最大值均降至许用范围内。结论对热封机构优化设计采用组合优化策略可行,结果可信度高,效果明显。     

Effective multi-objective optimization with the coral reefs optimization algorithm

In this article a new algorithm for multi-objective optimization is presented, the Multi-Objective Coral Reefs Optimization (MO-CRO) algorithm. The algorithm is based on the simulation of processes in coral reefs, such as corals' reproduction and fight for space in the reef. The adaptation to multi-objective problems is a process based on domination or non-domination during the process of fight for space in the reef. The final MO-CRO is an easily-implemented and fast algorithm, simple and robust, since it is able to keep diversity in the population of corals (solutions) in a natural way. The experimental evaluation of this new approach for multi-objective optimization problems is carried out on different multi-objective benchmark problems, where the MO-CRO has shown excellent performance in cases with limited computational resources, and in a real-world problem of wind speed prediction, where the MO-CRO algorithm is used to find the best set of features to predict the wind speed, taking into account two objective functions related to the performance of the prediction and the computation time of the regressor.     

Optimal feeding profile in fed-batch bioreactors using a genetic algorithm

An optimal feeding profile for a fed-batch process was designed based on an evolutionary algorithm. Usually the presence of multiple objectives in a problem leads to a set of optimal solutions, commonly known as Pareto-optimal solutions. Evolutionary algorithms are well suited for deriving multi-objective optimisation since they evolve a set of non-dominated solutions distributed along the Pareto front. Several evolutionary multi-objective optimisation algorithms have been developed, among which the Non-dominated Sorting Genetic Algorithm NSGA-II is recognised to be very effective in overcoming a variety of problems. To demonstrate the applicability of this technique, an optimal control problem from the literature was solved using several methods considering the single-objective dynamic optimisation problem.     

A new hybrid imperialist competitive algorithm on data clustering

Clustering is a process for partitioning datasets. This technique is very useful for optimum solution. k-means is one of the simplest and the most famous methods that is based on square error criterion. This algorithm depends on initial states and converges to local optima. Some recent researches show that k-means algorithm has been successfully applied to combinatorial optimization problems for clustering. In this paper, we purpose a novel algorithm that is based on combining two algorithms of clustering; k-means and Modify Imperialist Competitive Algorithm. It is named hybrid K-MICA. In addition, we use a method called modified expectation maximization (EM) to determine number of clusters. The experimented results show that the new method carries out better results than the ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and k-means.     

An Adaptive Classifier Based Approach for Crowd Anomaly Detection

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video sections. We use the multi-objective whale optimization algorithm to optimize the entire process and get the best results. The performance parameters such as accuracy, precision, recall, and F-score are considered to evaluate the proposed technique using the Python simulation tool. Our simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.     

A novel approach using Dynamic Social Impact Theory for optimization of impedance-Tongue (iTongue)

This paper presents a novel multiobjective wrapper approach using Dynamic Social Impact Theory based optimizer (SITO). A Fuzzy Inference System in conjunction with support vector machines classifier has been used for the optimization of an impedance-Tongue for the classification of samples collected from single batch production of Kangra orthodox black tea. Impedance spectra of the tea samples have been measured in the range of 20 Hz to 1 MHz using a two electrode setup employing platinum and gold electrodes. The proposed approach has been compared, for its robustness and validity using various intra and inter measures, against Genetic Algorithm and binary Particle Swarm Optimization. Feature subset selection methods based on the first and second order statistics have also been employed for comparisons. The proposed approach outperforms the Genetic Algorithm and binary Particle Swarm Optimization.     

Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design

In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems.     

生产调度与维护集成的多目标Lorenz非劣遗传优化

研究了一种单机环境下集成生产和维护的双目标优化调度问题。机床的故障间隔时间和平均维修时间服从指数分布,同时结合加工序列相关准备时间。预防性维护活动不能与作业加工同时进行,但与准备时间不相冲突。调度目标是同时最小化作业总计完成时间和机床不可得性。在问题建模的基础上,构造了一种基于Lorenz非劣关系的分类遗传算法(表示为L-NSGA-Ⅱ),详细设计了算法的核心部分。最后,通过大量计算实验,将L-NSGA-II算法与NSGA-II算法进行了比较分析,说明了L-NSGA-II算法的有效性。