Solving multi-objective cell design problem: an evolutionary genetic algorithm approach

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

Due to the conflict between reducing cost and improving water supply performance, how to select the appropriate pipe diameter is a current challenge. In this paper, the problem is transformed into a multi-objective optimization problem, and the evolutionary genetic optimization algorithm is used to solve the problem to determine the optimal selection of pipe diameter in the pipe network. To solve this problem, the evolutionary genetic algorithm was coupled with EPANET hydraulic simulation software in Python environment. The results show that NSGA-II and NSGA-III perform better in two typical case tests. Moreover, the increase of the objective functions will lead to an increase in the amount of data in the optimal solution set, and will affect the optimal value of each objective function. That shows that the balance between the economy and reliability of water supply can be successfully found by coupling the hydraulic model and the multi-objective optimization algorithm, which can provide an auxiliary decision for enterprises.

The formation of machine–part families is an important task in the design of cellular and flexible manufacturing systems. The formation results in the creation of many benefits for manufacturing systems. Among the many methods utilised in machine–cells formation, the similarity coefficient method (SCM) is most widely used. When SCM is used, the rearrangement of machine and part components is required to form machine–part families. This process of rearrangement has been considered as being subjective and difficult, may result in improper assignments to parts families, resulting in a negation of benefits promised. This paper presents an effective algorithm to identify part–families and bottleneck parts, given machine groupings, rather than addressing the machine grouping problem in general.

This chapter reviews and expands our work on the relationship between linkage structure, that is how decision variables of a problem are linked with (dependent on) one another, and the performance of three basic types of genetic evolutionary algorithms (GEAs): hill climbing, genetic algorithm and bottom-up self-assembly (compositional). It explores how concepts and quantitative methods from the field of social/complex networks can be used to characterize or explain problem difficulty for GEAs. It also re-introduces two novel concepts – inter-level conflict and specificity – which view linkage structure from a level perspective. In general, the basic GEAs performed well on our test problems with linkage structures resembling those empirically observed in many real-world networks. This is a positive indication that the structure of real-world networks which evolved without any central organization such as biological networks is not only influenced by evolution and therefore exhibit non-random properties, but also influences its own evolution in the sense that certain structures are easier for evolutionary forces to adapt for survival. However, this necessarily implies the difficulty of certain other structures. Hence, the need to go beyond basic GEAs to what we call GEAs with “brains”, of which linkage-learning GEAs is one species.

In this paper, we use the genetic algorithms (GA) technique to handle two problems in the manufacturing systems: the formation of manufacturing cell in cellular manufacturing and batch scheduling. In the formation of machine cells, we use multi-objective functions as criteria to form cell. These criteria are to minimise the inter-cell movement, to minimise variation of workload within cells and to maximise the similarity within cells. Results showed that by varying the weights of each criterion, the algorithm produced is flexible to adapt to changes in the manufacturing environment. Unlike traditional methods which merely rearrange the part-incidence matrix, this algorithm incorporates other parameters such as processing times of each part and number of parts required. The batch scheduling problem described in this paper is the problem of scheduling a single-machine with jobs of different due-dates and arrival times. We have developed an algorithm which is not only able to find the optimum or near-optimal sequence of jobs, but also able to determine the number of jobs to be processed in each batch. The objective function used is to minimise total tardiness.

Optimal machining parameters are very important for every machining process. This paper presents an Evolutionary Multi-objective Genetic Algorithm based optimization technique to optimize the machining parameters (cutting speed, feed and depth of cut) in a turning process. The effect of these parameters on production time, production cost and surface roughness (which are conflicting to each other) are mathematically formulated. The non-dominated sorting genetic algorithm (NSGA-II) is used to get a Pareto-optimal front of the machining problem. The Pareto-optimal points are checked using e-constraint single objective GA as well as using a classical optimization (SQP) method. An analysis of the obtained points is carried out to find the useful relation between the objective function and variable values.

In recent decades, evolutionary optimisation algorithms have been used successfully for a wide variety of water resources engineering problems and their applications are still increasing. In this research work, a hybrid harmony search algorithm, ‘Non‐dominated Sorting Harmony Search’ algorithm is developed and compared with two state‐of‐the‐art multi‐objective evolutionary algorithms – the non‐dominated sorting genetic algorithm (NSGA)‐IIand multi‐objective particle swarm optimisation (MOPSO) algorithms – for assigning optimal rehabilitation plans for sewer pipe networks. The algorithms considered were validated using some standard test functions reported in the literature and compared with each other in terms of several metrics. These algorithms were then linked to theSWMM‐EPAhydraulic model and applied to a storm sewer pipe network case study in Seoul, South Korea, to obtain the best rehabilitation plans for pipe replacements. The results showed that the algorithms considered have different behaviours in solving the benchmark tests and rehabilitation problem. The proposed hybrid multi‐objective harmony search algorithm provides better optimal solutions in terms of different metrics and clearly outperforms the other two algorithms for the rehabilitation of the storm sewer pipe networks.

A project scheduling problem investigates a set of activities that have to be scheduled due to precedence priority and resource constraints in order to optimize project-related objective functions. This paper focuses on the multi-mode project scheduling problem concerning resource constraints (MRCPSP). Resource allocation and leveling, renewable and non-renewable resources, and time-cost trade-off are some essential characteristics which are considered in the proposed multi-objective scheduling problem. In this paper, a novel hybrid algorithm is proposed based on non-dominated sorting ant colony optimization and genetic algorithm (NSACO-GA). It uses the genetic algorithm as a local search strategy in order to improve the efficiency of the ant colony algorithm. The test problems are generated based on the project scheduling problem library (PSPLIB) to compare the efficiency of the proposed algorithm with the non-dominated sorting genetic algorithm (NSGA-II). The numerical result verifies the efficiency of the proposed hybrid algorithm in comparison to the NSGA-II algorithm.

The highly competitive and volatile market puts companies in a tough position. While cost and time efficiency are important to stay competitive, environmental awareness is more and more critical. The reconfigurable manufacturing system (RMS) paradigm is suggested to cope with these new challenges. In addition to its six fundamental characteristics, it is seen as an enabler for Industry 4.0. This article investigates the multi-objective process planning problem in an environmentally conscious manner in a reconfigurable manufacturing environment. Four criteria are minimised: total production cost, total production time, total amount of greenhouse gas produced by machines, and total quantity of hazardous liquid wastes. To address the problem, modified versions of the non-dominated sorting genetic algorithm (NSGA) method, namely new dynamic NSGA-II (NewD-NSGA-II) and New NSGA-III, are developed and evaluated. Rich experimental results are presented and analysed using three metrics to demonstrate the efficacy of the proposed approaches: inverted generational distance (IGD), diversity measure (DM), and cardinality of the mixed Pareto fronts (CMPF). The effects of the similarity coefficient on the convergence of the NewD-NSGA-II and New NSGA-III are investigated, and the TOPSIS technique is used to assist the decision-maker in evaluating and selecting the best process plans.

Batch scheduling is a well-known topic that has been studied widely with various objectives, methods, and circumstances. Unfortunately, batch scheduling in a collaborative flow shop system is still unexplored. All studies about batch scheduling that are found were in a single flow shop system where all arriving jobs come from single door. In a collaborative flow shop system, every flow shop handles its own customers although joint production among flow shops to improve efficiency is possible. This work aims to develop a novel batch scheduling model for a collaborative multi-product flow shop system. Its objective is to minimize make-span and total production cost. This model is developed by using non-dominated sorting genetic algorithm (NSGA II) which is proven in many multi objective optimization models. This model is then compared with the non-collaborative models which use NSGA II and adjacent pairwise interchange algorithm. Due to the simulation result, the proposed model performs better than the existing models in minimizing the make-span and total production cost. The make-span of the proposed model is 10 to 17 percent lower than the existing non-collaborative models. The total production cost of the proposed model is 0.3 to 3.5 percent lower than the existing non-collaborative models.

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

The quadcopter is an unmanned aerial vehicle that has been used in civil and military applications because of its relatively low cost, simple design, and ease of maintenance in addition to its unique flying characteristics and vast potential. Six Proportional-Integral-Derivative (PID) controllers are usually built for the position and attitude control of the quadcopter. However, tuning the controllers’ parameters poses a real challenge, as the system is highly coupled and contains two cascaded control loops while being underactuated (only four control inputs for the six degrees of freedom). In this study, a quadcopter model has been implemented by using MATLAB / SIMULINK where the model has been constructed to be integrated with the evolutionary genetic algorithm in addition to a third-party toolbox that is used to simulate fractional order calculus. The gains of the traditional and the fractional-order proportional-integral-derivative controllers have been tuned by using the genetic algorithm against different path shapes. The performance of the manually and the genetic algorithm tuned proportional-integral-derivative controllers have been compared with the genetic algorithm tuned fractional-order proportional-integral-derivative controllers. The genetic algorithm tuned fractional-order proportional-integral-derivative controllers have been superior and more robust in all cases with a lower fitness function value, much smother responses, and higher trajectory tracking ability.

An evolutionary algorithm for manufacturing cell formation

Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.

Non-dominated sorting genetic algorithm has shown excellent advantages in solving complicated optimization problems with discrete variables in a variety of domains. In this paper, we implement a multi-objective genetic algorithm to guide the design of the laminated structure with two objectives: minimizing the mass and maximizing the strength of a specified structure simultaneously, classical lamination theory and failure theory are adopted to compute the strength of a laminate. The simulation results have shown that a non-dominated genetic algorithm has great advantages in the design of laminated composite material. Experiment results also suggest that optimal run times are from 16 to 32 for the design of glass-epoxy laminate with non-dominated sorting genetic algorithm. We also observed that two stages involve the optimization process in which the number of individuals in the first frontier first increases, and then decreases. These simulation results are helpful to decide the proper run times of genetic algorithms for glass-epoxy design and reduce computation costs.