Objective Evolutionary Algorithm Research Articles

Reinforcement learning for distributed hybrid flowshop scheduling problem with variable task splitting towards mass personalized manufacturing

Mass personalization manufacturing (MPM), an emerging production pattern, aims to improve enterprise profit in modern industries. However, the processing of heterogeneous orders from the consumers complicates such production scheduling problem. In addition, different scale tasks should adopt different splitting strategies in practical manufacturing, which makes the task splitting method more worthy of investigation. Towards MPM, this paper presents a distributed hybrid flowshop scheduling problem with variable task splitting (DHFSP-VTS) to minimize the makespan and total energy consumption simultaneously. Meanwhile, the VTS allows the tasks to be split into different sublots so they can save setup and transfer time. To solve these problems, we present an order modularization processing method that can categorize multiple types of orders into specific generation tasks, and a highly effective reinforcement learning-multiple objective evolutionary algorithm based on decomposition (RL-MOEA/D) is designed. In RL-MOEA/D, there are three features: (1) three initial rules are used for initialization based on the current splitting scheme that can increase the diversity of solutions; (2) the reinforcement learning agent uses the Q-learning mechanism to dynamically select the scheme of task splitting as action; (3) a neighborhood search strategy improves the exploitation ability and expand the solution space. To verify the effectiveness of RL-MOEA/D, the MOEA/Ds based on four splitting schemes and four RL combined meta-heuristics are compared on 18 instances. The results show that RL-MOEA/D can obtain the best optimization and stability of all the other comparison algorithms. Therefore, it’s a new technique to solve DHFSP with large-scale tasks, especially for the problem of MPM.

DNA sequences design under many objective evolutionary algorithm

DNA sequences design under many objective evolutionary algorithm

A Many-Objective Evolutionary Algorithm with Local Shifted Density Estimation Based on Dynamic Decomposition

Pareto dominance-based many-objective evolutionary algorithms (PDMaOEAs) are challenging in dealing with many-objective problems (MaOPs) encountering many incomparable nondominated solutions. Recently, convergence-related metrics have been incorporated into PDMaOEAs to enhance the selection pressure approaching the true Pareto front and furtherly improve the balance of diversity and convergence, however these approaches still have limitations. To address the drawbacks of the previous approaches, a many objective evolutionary algorithm with local shifted density estimation based on dynamic decomposition (MaOEA/LSD-DD) is proposed in this paper. The proposed MaOEA/LSD-DD maintains the convergence and diversity of populations through the dynamic synergy of dynamic decomposition and shifted density estimation. First, identifying potential regions through dynamic decomposition can reduce redundant evaluation to save computational resources and maintain good distribution. Then, shifted density estimation is executed in potential regions to selected the individuals with good diversity and convergence into next generation population. Finally, this process is repeated until the population size is satisfied. The performance of the MaOEA/LSD-DD is investigated extensively on 28 test problem from three popular benchmark problem suites and a practical problem of water resource planning problem by comparing them with seven excellent MaOEAs. The experimental results show the effectiveness of the proposed MaOEA/LSD-DD in keeping a good tradeoff between diversity and convergence over other MaOEAs when solving most of these MaOPs.

Novel multi objective evolutionary framework for solving next release problem

Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the non-repetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works’ results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published.

Dynamic multi‐objective optimisation of complex networks based on evolutionary computation

AbstractAs the problems concerning the number of information to be optimised is increasing, the optimisation level is getting higher, the target information is more diversified, and the algorithms are becoming more complex; the traditional algorithms such as particle swarm and differential evolution are far from being able to deal with this situation effectively, and the multi‐objective evolutionary algorithm (MOEA) was born. Multi‐objective evolutionary algorithms help users to quickly obtain the data they want from the huge amount of complex network data, which greatly improves the efficiency. The multi objective evolutionary algorithm (MOEA) is simple, effective, and versatile, making it extremely attractive when solving multi‐objective optimisation problems. Since the distribution of the initial population affects the accuracy of the algorithm to some extent, this paper proposes to combine the mathematical calculus of evolutionary computation with the dynamics of complex networks as a way to carry out multi‐objective optimisation problems and find a more reasonable way for human information extraction. .The experimental results show that the set of non‐dominated solutions obtained by the algorithms designed is closer to the Pareto frontier, and the distribution of the searched non‐dominated solutions is more uniform.

Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

<span>The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.</span>

A Multi Objective Evolutionary Algorithm based on Decomposition for a Flow Shop Scheduling Problem in the Context of Industry 4.0

Under the novel paradigm of Industry 4.0, missing operations have arisen as a result of the increasingly customization of the industrial products in which customers have an extended control over the characteristics of the final products. As a result, this has completely modified the scheduling and planning management of jobs in modern factories. As a contribution in this area, this article presents a multi objective evolutionary approach based on decomposition for efficiently addressing the multi objective flow shop problem with missing operations, a relevant problem in modern industry. Tests performed over a representative set of instances show the competitiveness of the proposed approach when compared with other baseline metaheuristics.

Multi-objective complex product assembly scheduling problem considering parallel team and worker skills

The flow shop scheduling problem (FSP) is a classic shop scheduling problem with a strong engineering background. As an extension of FSP, the hybrid flow shop scheduling problem (HFSP) involves parallel machine scheduling, and the worker assignment problem on assembly lines (WAPAL) involves worker allocation. Parallel machine scheduling and multi-skilled and multilevel worker allocation are both involved in the actual assembly lines of complex products, but few studies have investigated them simultaneously. This work studies a complex product assembly line scheduling problem considering multi-skilled worker assignment and parallel team scheduling and takes the maximum completion time and the imbalance degree of team workload as the optimization objective. An integer programming model is proposed, and a hybrid coding method considers the worker assignment and task order. Three improved strategies based on a multiple objective evolutionary algorithm (MOEA) are proposed in the local search. Finally, 20 test instances are generated based on actual enterprise labor data, and the results based on the three strategies are compared with six MOEAs. The results show that the three strategies are superior in terms of the quality and distribution of solutions. The inverted generational distance (IGD) index value is increased by 49.97%, 47.89%, 47.08% respectively and the hypervolume (HV) index value is increased by 39.76%, 38.19%, 38.15% respectively.

Objective space division-based hybrid evolutionary algorithm for handing overlapping solutions in combinatorial problems.

Overlapping solutions occur when more than one solution in the space of decisions maps to the same solution in the space of objectives. This situation threatens the exploration capacity of Multi-Objective Evolutionary Algorithms (MOEAs), preventing them from having a good diversity in their population. The influence of overlapping solutions is intensified on multi-objective combinatorial problems with a low number of objectives. This paper presents a hybrid MOEA for handling overlapping solutions that combines the classic NSGA-II with a strategy based on Objective Space Division (OSD). Basically, in each generation of the algorithm, the objective space is divided into several regions using the nadir solution calculated from the current generation solutions. Furthermore, the solutions in each region are classified into non-dominated fronts using different optimization strategies in each of them. This significantly enhances the achieved diversity of the approximate front of non-dominated solutions. The proposed algorithm (called NSGA-II/OSD) is tested on a classic Operations Research problem: the Multi-Objective Knapsack Problem (0-1 MOKP) with two objectives. Classic NSGA-II, MOEA/D and Global WASF-GA are used to compare the performance of NSGA-II/OSD. In the case of MOEA/D two different versions are implemented, each of them with a different strategy for specifying the reference point. These MOEA/D reference point strategies are thoroughly studied and new insights are provided. This paper analyses in depth the impact of overlapping solutions on MOEAs, studying the number of overlapping solutions, the number of solution repairs, the hypervolume metric, the attainment surfaces and the approximation to the real Pareto front, for different sizes of 0-1 MOKPs with two objectives. The proposed method offers very good performance when compared to the classic NSGA-II, MOEA/D and Global WASF-GA algorithms, all of them well-known in the literature.

Design of Cross Coupled Non-Linear Pid Controller Using Single Objective Evolutionary Algorithms

Design of Cross Coupled Non-Linear Pid Controller Using Single Objective Evolutionary Algorithms

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

Pearson coefficient matrix for studying the correlation of community detection scores in multi-objective evolutionary algorithm

Assessing a community detection algorithm is a difficult task due to the absence of finding a standard definition for objective functions to accurately identify the structure of communities in complex networks. Traditional methods generally consider the detecting of community structure as a single objective issue while its optimization generally leads to restrict the solution to a specific property in the community structure. In the last decade, new community detection models have been developed. These are based on multi-objective formulation for the problem, while ensuring that more than one objective (normally two) can be simultaneously optimized to generate a set of non-dominated solutions. However the issue of which objectives should be co-optimized to enhance the efficiency of the algorithm is still an open area of research. In this paper, first we generate a candidate set of partitions by saving the last population that has been generated using single objective evolutionary algorithm (SOEA) and random partitions based on the true partition for a given complex network. We investigate the features of the structure of communities which found by fifteen existing objectives that have been used in literature for discovering communities. Then, we found the correlation between any two objectives using the pearson coefficient matrix. Extensive experiments on four real networks show that some objective functions have a strong correlation and others either neutral or weak correlations.

Evolutionary Optimization based Solution approaches for Many Objective Reliability-Redundancy Allocation Problem

Recently, a number of evolutionary optimization approaches were proposed to solve the many objective problems. The reliability redundancy allocation problem (RRAP), which usually has four different objectives, namely, maximization of system reliability, minimizations of cost, weight and volume, are however solved mainly as a multi-objective problem considering only two or three objectives. Therefore, this paper reports a novel study of the RRAP as a many objective optimization problem. Here, we formulate the many objective RRAP (MaORRAP) with various structures such as series-parallel systems, overspeed gas turbine system, and large-scale system. For the formulated MaORRAP, we then provide the details of a novel solution procedure based on the non-dominated sorting genetic algorithm-III (NSGA-III), a well-discussed many objective evolutionary optimization algorithm. We also solve MaORRAP using three other popular evolutionary approaches, viz. non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEA/D) and strength Pareto evolutionary archive 2 (SPEA2) algorithm. Accordingly, we present all the results in a competitive fashion to have a thorough comparative assessment of the performances of the considered approaches and show that, in most of the cases, NSGA-III based solutions are superior to others.

A Wasserstein distance based multiobjective evolutionary algorithm for the risk aware optimization of sensor placement

• A Multiobjective Evolutionary Algorithm for the sensor placement (SP) in a network. • A novel data structure for the spatio-temporal contamination dynamics. • The fitness of a SP is represented as the histogram of a discrete random variable. • The Wasserstein distance between SP's enables new genetic operators. In this paper we propose a new algorithm for the identification of optimal “sensing spots”, within a network, for monitoring the spread of “effects” triggered by “events”. This problem is referred to as “Optimal Sensor Placement” and many real-world problems fit into this general framework. In this paper sensor placement (SP) (i.e., location of sensors at some nodes) for the early detection of contaminants in water distribution networks (WDNs) will be used as a running example. Usually, we have to manage a trade-off between different objective functions, so that we are faced with a multi objective optimization problem. (MOP). The best trade-off between the objectives can be defined in terms of Pareto optimality. In this paper we model the sensor placement problem as a multi objective optimization problem with boolean decision variables and propose a Multi Objective Evolutionary Algorithm (MOEA) for approximating and analyzing the Pareto set. The evaluation of the objective functions requires the execution of a simulation model: to organize the simulation results in a computationally efficient way we propose a data structure collecting simulation outcomes for every SP which is particularly suitable for visualization of the dynamics of contaminant concentration and evolutionary optimization. This data structure enables the definition of information spaces, in which a candidate placement can be represented as a matrix or, in probabilistic terms as a histogram. The introduction of a distance between histograms, namely the Wasserstein (WST) distance, enables to derive new genetic operators, indicators of the quality of the Pareto set and criteria to choose among the Pareto solutions. The new algorithm MOEA/WST has been tested on two benchmark water distribution networks and a real world network. Preliminary results are compared with NSGA-II and show a better performance, in terms of hypervolume and coverage, in particular for relatively large networks and low generation counts.

A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks

The sensor placement problem is modeled as a multi-objective optimization problem with Boolean decision variables. A new multi objective evolutionary algorithm (MOEA) is proposed for approximating and analyzing the set of Pareto optimal solutions. The evaluation of the objective functions requires the execution of a hydraulic simulation model of the network. To organize the simulation results a data structure is proposed which enables the dynamic representation of a sensor placement and its fitness as a heatmap. This allows the definition of information spaces, in which the fitness of a placement can be represented as a matrix or, in probabilistic terms as a histogram. The key element in the new algorithm is this probabilistic representation which is embedded in a space endowed with a metric based on a specific notion of distance. Among several distances between probability distributions the Wasserstein (WST) distance has been selected: WST has enabled to derive new genetic operators, indicators of the quality of the Pareto set and criteria to choose among the Pareto solutions. The new algorithm has been tested on a benchmark water distribution network with two objective functions showing an improvement over NSGA-II, in particular for low generation counts, making it a good candidate for expensive black-box multi-objective optimization

A novel multiple objective whale optimization for time-cost-quality tradeoff in non-unit repetitive projects

Project managers are the ones who struggle to complete the project with the shortest possible time, the least amount of costs and, the highest quality. The primary objective in the planning stage of the project management process is to identify the most suitable schedule from provided time–cost-quality curve. Scheduling of construction repetitive projects assumes the same production and hard logic between units. This study proposes a free-parameter multiple objective whale optimization (MOWO) to tradeoff the time, cost and, quality factors in non-unit repetitive projects (NRP). An external scheduling system shall consider project activities dependencies, multiple crew assignments, and soft logic in units to calculate project duration, cost and, quality. A construction case study was used to examine MOWO performance. Furthermore, some broadly recognized multiple objective evolutionary algorithms are employed to compare with the proposed algorithm. Result comparison points out that MOWO attained the best optimization outcomes in handling the time-cost-quality tradeoff in non-unit repetitive construction projects. Highlights This study presents an advanced multiple optimization algorithm MOWO. A scheduling subsystem is proposed to enable TCQ tradeoff in non-unit repetitive projects. MOWO is applied to solve TCQ for non-unit repetitive construction projects. The model performance is demonstrated in the experimental results.

Hybrid neighborhood and global replacement strategies for multi objective evolutionary algorithm based on decomposition

In multi-objective evolutionary algorithm based on decomposition, replacement strategy (RS) plays a key role in balancing population convergence and diversity. However, the existing RSs either focus on the neighborhood or global replacement strategy, which may have difficulties in solving the complex multi-objective optimization problems. To solve this problem, a hybrid neighborhood and global replacement strategies for multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed. In this mechanism, a probability threshold pt is used to determine whether to implement neighborhood or global replacement strategy to balance convergence and diversity. Meanwhile, an offspring generation method is designed to generate high-quality offspring solutions for each sub-problem, which can improve the success of the replacement strategy. Based on MOEA/D, a new MOEA/D-HRS algorithm is designed, and it is compared with some state-of-the-art multi-objective evolutionary algorithms (MOEAs) in a series of bi-objective and three-objective test instances with linear or nonlinear various linkages. The experimental results show that MOEA/D-HRS can achieve the best performance in most test instances.

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites.

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.

Multiple Objective Social Group Optimization for Time–Cost–Quality–Carbon Dioxide in Generalized Construction Projects

This study presents a novel approach named as “multiple objective social group optimization” (MOSGO) to tradeoff time, cost, quality, and carbon dioxide emission (TCQC) factors in generalized construction projects. The proposed algorithm modifies the operation mechanism to balance the exploration and exploitation abilities of the optimization process. The TCQC tradeoff problem considers all types of logical relationships between project activities. Two practical case studies demonstrate the ability of MOSGO-generated, non-dominated solutions. In addition, evidential reasoning is applied to select a compromise solution for project implementation. Comparisons between the MOSGO and four well-known algorithms (MODE, MOABC, MOPSO, and NSGA-II) to verify the efficiency and effectiveness of the developed algorithm. According to the statistical analysis, the proposed algorithm generated the highest values of diversification measurement (DM) of 26.113 and 40.27; the highest values of hyper-volume (HV) of 0.875 and 0.881 in case 1 and case 2, respectively. The proposed algorithm also found solutions with lowest mean ideal distance (MID) and spread (SP) values of 0.872 and 0.462 in the first case and of 0.754 and 0.689 in the second case. MOSGO showed had better diversify and convergence, gained wider spread, and yielded higher uniformity of solutions than the compared multiple objective evolutionary algorithms.

Evolutionary multi-objective set cover problem for task allocation in the Internet of Things

Efficient distribution of tasks in an Internet of Things (IoT) network ensures the fulfillment for all objects to dynamically cooperate with their limited energy, processing and memory capabilities. The main contribution of this paper is threefold. Firstly, we address the task allocation in the IoT as an optimization problem with a new formulation derived from the context of set cover problem. To the best of our knowledge, no such study has been considered in the literature. Secondly, we extend the set cover problem to further express the conflict that meets with both operational period and stability. Thirdly, an evolutionary single objective and multi-objective algorithms are developed to tackle the formulated problem. Two heuristic operators are also introduced and injected within the framework of the evolutionary algorithms where the need arises to harness their strength in terms of both operational period and network stability. Performance evaluation is reported while different problem dimensions are experimented with in the simulations. The results show that the proposed multi-objective evolutionary algorithm is quite appropriate to converge to more accurate solutions than the counterpart single objective evolutionary algorithm. Further, the results give plausible evidence supporting the importance of the proposed heuristic operators to mitigate against the contradictory nature of the network lifetime in terms of operational period and stability.