Pareto-based Optimization Algorithm Research Articles

A hybrid intelligent algorithm for a fuzzy multi-objective job shop scheduling problem with reentrant workflows and parallel machines

This paper presents a multi-objective mathematical model for a flexible job shop scheduling problem (FJSSP) with fuzzy processing times, which is solved by a hybrid intelligent algorithm (HIA). This problem contains a combination of a classical job shop problem with parallel machines (JSPM) to provide flexibility in the production route. Despite the previous studies, the number of parallel machines is not pre-specified in this paper. This constraint with other ones (e.g., sequence-dependent setup times, reentrant workflows, and fuzzy variables) makes the given problem more complex. To solve such a multi-objective JSPM, Pareto-based optimization algorithms based on multi-objective meta-heuristics and multi-criteria decision making (MCDM) methods are utilized. Then, different comparison metrics (e.g., quality, mean ideal distance, and rate of achievement simultaneously) are used. Also, this paper includes two major phases to provide a new model of the FJSSP and introduce a new proposed HIA for solving the presented model, respectively. This algorithm is a hybrid genetic algorithm with the SAW/TOPSIS method, namely HGASAW/HGATOPSIS. The comparative results indicate that HGASAW and HGATOPSIS outperform the non-dominated sorting genetic algorithm (NSGA-II) to tackle the fuzzy multi-objective JSPM.

A Pareto-based optimization algorithm for a multi-objective integrated production-distribution planning problem of a multi-echelon supply chain network design

This paper addresses a multi-periodic supply chain network design (SCND) problem involving suppliers, manufacturers, distribution centres (DCs), and customer zones (CZs). Logistic decisions made in each time period have a tactical nature. Location/strategic decisions are made at the beginning of the time horizon and remain unchanged until the last period. While both backorders and lost sales are considered, the aim is to design the supply chain network (SCN) under three minimisation objectives including total costs, the transfer time of products to CZs, and backorder level and lost sale of products. A multi-objective mixed-integer linear programming (MILP) model is developed and a meta-heuristic algorithm named multi-objective vibration damping optimisation (MOVDO) with tuned parameters is proposed to find non-dominated solutions. The performance of this method is compared with two popular existing algorithms called NSGA-II and NRGA when they solve some randomly generated problems.

Optimal Allocation of Measurement Devices for Distribution State Estimation Using Multiobjective Hybrid PSO–Krill Herd Algorithm

This paper proposes a new multiobjective hybrid particle swarm optimization (PSO)–krill herd (KH) Pareto-based optimization algorithm to optimize number and location of the measurement devices for accurate state estimation (SE) in smart distribution networks. Three objectives are considered to be minimized: 1) the total cost; 2) the average relative percentage error (APE) of bus voltage magnitude; and 3) APE of bus voltage angle. As the objective functions are conflicting with respect to each other, a multiobjective Pareto-based nondominated sorting hybrid PSO–KH optimization algorithm is proposed. In this approach, the random variation in loads and the metrological error of the measurement devices are also taken into account. The proposed algorithm minimizes the cost and enhances the accuracy of the distribution state estimator for better monitoring and control of the system. Furthermore, the impacts of distributed generation on SE performance are also investigated. The feasibility of the proposed algorithm is demonstrated on IEEE 69-bus system and practical Indian 85-bus radial distribution network. The results obtained are compared with conventional KH algorithm and PSO, with well-known multiobjective nondominated sorting genetic algorithm and also with an existing technique based on dynamic programming method for validation.

FEM model-based optimal control synthesis for curing a large composite structure with CAD imported geometry

The paper studies a problem of optimal control synthesis for curing a large composite structure using open die moulding in an autoclave. The necessary control should eliminate early hard skin formation, emergence of resin-rich or resin-dry areas, insufficient consolidation, and uneven cure. This purpose is achieved through providing uniform distribution of degree of cure and temperature within the cured part volume. The used approach is illustrated on the example of the large CFRP aircraft panel manufactured by means of two-stage curing cycle. The forward problem combines the heat transfer and autocatalytic kinetic equations, which are linked by the specific heat capacity, thermal conductivity coefficient and by the exothermal heat source that depend on the prepreg’s degree of cure. The control synthesis problem is formulated as a multi-objective optimization problem where minimized objectives are deviations of temperature and degree of cure within a cured part, considering constraints imposed by thermal-kinetic properties of prepreg and manufacturing requirements. The Pareto-based optimization algorithm, which executes the mapping of a subset of design space to the objectives space by the cyclic calling the forward problem implemented as a finite element model, allows estimating the best achievable quality indicators of manufactured composite parts, finding satisfactory parameters of the control law, and decision-making considering all imposed constraints.

Evolving Optimal and Diversified Military Operational Plans for Computational Red Teaming

Computational Red teaming (CRT) is a simulation-based optimization application utilized by defense analysts to uncover vulnerabilities of operational plans. In CRT, agent-based simulation models of military scenarios are automatically analyzed and modeled using evolutionary computation techniques. The CRT optimization process aims at identifying simulation models which exhibit emergent system behaviors of interest, e.g., when the adversary (called “Red”) breaks the defensive (“Blue”) strategies. Numerous multiobjective evolutionary algorithms (MOEAs) have been applied to CRT; however, the elitist and converging nature of these Pareto-based optimization algorithms typically leads to the generation of optimal, with respect to the Pareto front, but poorly diversified adversarial operational plans. As a result, the near-optimal alternative strategies are omitted; this considerably limits the applicability of CRT when considering the decision makers point of view. We propose a diversity enhancement scheme for MOEAs which uses the diversity contribution of individual solutions in the aggregated (combining both the objective and decision variable spaces) space to compute the fitness assignment. This feature enables both the exploitation of Pareto-optimal solutions whilst promoting diversification of the solutions in the decision variable space. Our experimental results indicate that this diversity enhancement mechanism can effectively resolve the diversification issue and, ultimately, enhance CRT to assist decision making.

Pareto-Based Optimization of Part Orientation in Stereolithography

This paper presents a Pareto-based optimization algorithm to determine a series of best part orientations in stereolithography systems. Previous methods could apply limit objective functions (OFs). Moreover, methods with multiple OFs had abstracted them into a single fitness function. A single fitness function never reflects the characteristics of individual OFs properly. The method proposed here handles several OFs individually. The objective functions are the build time and support volume under a desired surface finish. The optimization was performed using the multi-objective genetic algorithm (MOGA). At each genetic algorithm step, the surface finish was achieved by applying the adaptive layer thickness method. Pareto-based optimization finds a series of best part orientations with minimum build time and volume support. It is a multi-objective optimization that handles complex CAD files. The algorithm was developed using MATLAB. The codes were run for some case studies and the results were very promising.