Godfrey C. Onwubolu

New optimization techniques in engineering

Chapter 2: An Introduction to Genetic Algorithms for Engineering Applications Chapter 3: Memetic Algorithms Chapter 4: Scatter Search and Path Relinking: Foundations and Advanced Designs Chapter 5: Ant Colony Optimization Chapter 6: Differential Evolution Chapter 7: SOMA-Self-Organizing Migrating Algorithm Chapter 8: Discrete Particle Swarm Optimization:Illustrated by the Traveling Salesman Problem

Scheduling flow shops using differential evolution algorithm

This paper describes a novel optimization method based on a differential evolution (exploration) algorithm and its applications to solving non-linear programming problems containing integer and discrete variables. The techniques for handling discrete variables are described as well as the techniques needed to handle boundary constraints. In particular, the application of differential evolution algorithm to minimization of makespan, flowtime and tardiness in a flow shop manufacturing system is given in order to illustrate the capabilities and the practical use of the method. Experiments were carried out to compare results from the differential evolution algorithm and the genetic algorithm, which has a reputation for being very powerful. The results obtained have proven satisfactory in solution quality when compared with genetic algorithm. The novel method requires few control variables, is relatively easy to implement and use, effective, and efficient, which makes it an attractive and widely applicable approach for solving practical engineering problems. Future directions in terms of research and applications are given.

Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization

A new heuristic approach for minimizing the operating path of automated or computer numerically controlled drilling operations is described. The operating path is first defined as a travelling salesman problem. The new heuristic, particle swarm optimization, is then applied to the travelling salesman problem. A model for the approximate prediction of drilling time based on the heuristic solution is presented. The new method requires few control variables: it is versatile, robust and easy to use. In a batch production of a large number of items to be drilled such as in printed circuit boards, the travel time of the drilling device is a significant portion of the overall manufacturing process, hence the new particle swarm optimization–travelling salesman problem heuristic can play a role in reducing production costs.

Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization

This is the first book devoted entirely to Differential Evolution (DE) for global permutative-based combinatorial optimization. Since its original development, DE has mainly been applied to solving problems characterized by continuous parameters. This means that only a subset of real-world problems could be solved by the original, classical DE algorithm. This book presents in detail the various permutative-based combinatorial DE formulations by their initiators in an easy-to-follow manner, through extensive illustrations and computer code. It is a valuable resource for professionals and students interested in DE in order to have full potentials of DE at their disposal as a proven optimizer. All source programs in C and Mathematica programming languages are downloadable from the website of Springer.

Optimizing the multiple constrained resources product mix problem using genetic algorithms

The theory of constraints (TOC) is s a management philosophy for maximizing throughput. Since its introduction, many have criticized it as being inefficient when multiple constrained resources exist. The application of the five steps contained in TOC have been criticized by some researchers on the grounds that the application of five steps of TOC to the product mix decision leads to implicit or unrealizable solutions when multiple resource constraints in a plant exist. This paper views TOC as a management philosophy and a genetic algorithm-based TOC procedure is presented for solving combinatorial problems encountered in practice which cannot be solved using linear-integer programming or similar techniques. For smaller size problems, the results of the proposed procedure are compared with results of optimal methods published in the literature. The results are encouraging and therefore support the use of the proposed approach in an industrial setting.

Design of hybrid differential evolution and group method of data handling networks for modeling and prediction

This paper proposes a hybrid modeling approach based on two familiar non-linear methods of mathematical modeling; the group method of data handling (GMDH) and differential evolution (DE) population-based algorithm. The proposed method constructs a GMDH self-organizing network model of a population of promising DE solutions. The new hybrid implementation is then applied to modeling tool wear in milling operations and also applied to two representative time series prediction problems of exchange rates of three international currencies and the well-studied Box-Jenkins gas furnace process data. The results of the proposed DE-GMDH approach are compared with the results obtained by the standard GMDH algorithm and its variants. Results presented show that the proposed DE-GMDH algorithm appears to perform better than the standard GMDH algorithm and the polynomial neural network (PNN) model for the tool wear problem. For the exchange rate problem, the results of the proposed DE-GMDH algorithm are competitive with all other approaches except in one case. For the Box-Jenkins gas furnace data, the experimental results clearly demonstrates that the proposed DE-GMDH-type network outperforms the existing models both in terms of better approximation capabilities as well as generalization abilities. Consequently, this self-organizing modeling approach may be useful in modeling advanced manufacturing systems where it is necessary to model tool wear during machining operations, and in time series applications such as in prediction of time series exchange rate and industrial gas furnace problems.

Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification

Several pattern classifiers give high classification accuracy but their storage requirements and processing time are severely expensive. On the other hand, some classifiers require very low storage requirement and processing time but their classification accuracy is not satisfactory. In either of the cases the performance of the classifier is poor. In this paper, we have presented a technique based on the combination of minimum distance classifier (MDC), class-dependent principal component analysis (PCA) and linear discriminant analysis (LDA) which gives improved performance as compared with other standard techniques when experimented on several machine learning corpuses.

Tabu search-based algorithm for the TOC product mix decision

The theory of constraints (TOC) is a management philosophy that maximizes profits in a manufacturing plant with a demonstrated bottleneck. The product mix decision is one application of TOC that involves determination of the quantity and the identification of each product to produce. However, the original TOC heuristic is considered to produce unrealizable solution when a manufacturing plant has multiple resource constraints. This paper presents a tabu search-based TOC product mix heuristic to identify optimal or near optimal product mix for small problem instances under conditions where the original TOC heuristic failed. The tabu search-based TOC product mix heuristic is further used to solve large problem instances typical of practical manufacturing scenario. The experimental results for small to medium size problem show that the tabu search-based TOC heuristic compares favourably with those of optimal methods. Large size problems for which optimal methods have not been established in terms of feasibility in computation times were also solved in reasonable times with good quality solutions, thus confirming that the proposed approach is appropriate for adoption by production planners for the product mix problem in the manufacturing industry.

Genetic algorithm for minimizing tardiness in flow-shop scheduling

The present paper reports on a new approach to applying a genetic algorithm to the flow-shop scheduling problem. Three different objective functions considered are: minimizing total tardiness; minimizing number of tardy jobs; and minimizing both the above objective functions simultaneously. Two sets of solutions are presented; the first is based on a traditional heuristic, the second on a genetic algorithm metaheuristic. The former is suitable for relatively small-scale problem instances, while the latter finds very high quality optimum or near-optimum solution within a reasonably fast time, and is both effective and efficient for both medium- to large-scale problem instances. Results of computational testing are presented and confirm that the approach reported here is of high quality, fast for large problem instances, effective and efficient for flow-shop scheduling.

A genetic algorithm approach to the theory of constraints product mix problems

The goal of theory of constraints (TOC) is to maximize output, which is achieved by identifying and exploiting the critically constrained resource. However, application of the five steps of TOC to the product mix decision has been observed by several researchers to be implicit or result in infeasible solution when multiple resource constraints in a plant exist. This paper proposes a genetic algorithm-based approach in making the TOC problem explicit, and deals with large problem instances typical of manufacturing firms which have not been dealt with by known literature.