International Journal of Computational Intelligence and Applications

Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques.

This paper presents a procedural framework that unifies various mechanisms to look for discrete-neighborhood saddle points in solving discrete constrained optimization problems (DCOPs). Our approach is based on the necessary and sufficient condition on local optimality in discrete space, which shows the one-to-one correspondence between the discrete-space constrained local minima of a problem and the saddle points of the corresponding Lagrangian function. To look for such saddle points, we study various mechanisms for performing ascents of the Lagrangian function in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our results show that CSAEA, a combined constrained simulated annealing and evolutionary algorithm, performs well when using mutations and crossovers to generate trial points and accepting them based on the Metropolis probability. We apply iterative deepening to determine the optimal number of generations in CSAEA and show that its performance is robust with respect to changes in population size. To test the performance of the procedures developed, we apply them to solve some continuous and mixed-integer nonlinear programming (NLP) benchmarks and show that they obtain better results than those of existing algorithms.

In recent years we have evidenced an extensive effort in the development of computer communication networks, which have deeply integrated in human being's everyday life. One of the important aspects of the network design process is the topological design problem involved in establishing a communication network. However, with the increase of the problem scale, the conventional techniques are facing the challenge to effectively and efficiently solve those complicated network design problems.

In this article, we give a brief survey on our recent research works of network design problems by using genetic algorithms (GAs), such as, multistage process planning problem, fixed charge transportation problem, minimum spanning tree problem, centralized network design, and local area network design, shortest path problem, minimum cost maximum flow problem, and optimal scheduling problems based on network topology. All these problems are illustrated from the point of genetic representation encoding skill and the genetic operators with hybrid strategies. Numerical experiments show the effectiveness and efficiency of such kind of GA-based approach.

This work introduces a new asynchronous parallel self-adaptive evolution strategy. An asynchronous scheme is chosen because the load on the processors is usually unbalanced. The proposed algorithm is non-blocking leaving no processor idle at any given time. The motivation and theory for the algorithm is based on the author's previous analysis of continuous evolution strategies⁹ and the reduction of random fluctuation in mutative self-adaptation.⁷ The algorithm is implemented in C₊₊ using Posix threads and tested on a number of unimodal and multimodal benchmark problems. The result is an efficient and effective numerical optimization strategy for multi-processor machines.

This paper details the optimization of bulk material port handling systems through the use of an evolutionary based approach. The effective management of port systems requires effective solutions for the design, operation and maintenance of these facilities. This has to be achieved through a reduction in financial costs, and an increase in the utilization of equipment and other resources. The operation of a port system is complex and difficult to model mathematically. Consequently, through the explicit characterization of port components, a port modeling tool was developed that permits the generic construction of port simulation models. A genetic algorithm based approach was developed to provide an optimization capability to the port simulation tool. Two case studies based on real world port systems are presented and the results are discussed. A significant improvement is demonstrated in both the operational and economic performance as a result of the GA/model interaction.

In this paper the resource-constrained flow shop (RCF) problem is addressed. A number of realistic extensions are incorporated, including non-serial precedence requirements, mixed flow shop situations, and the distribution of the human workforce among a number of pre-determined groups. The RCF is then solved by meta-heuristics, primarily of the evolutionary type. An extensive numerical investigation, including a case study of a particular industrial situation, details the implementation and execution of the heuristics, and the efficiency of the proposed algorithms.

In this paper, we deal with the flexible job shop scheduling problem. We propose an efficient heuristic method for solving the assignment problem. Indeed, we propose a worst case analysis to evaluate the performance of such a heuristic. The second specificity of the problem studied is the sequencing property. Our approach consists in the application of an evolutionary algorithm based on a set of adapted operators to solve the sequencing step. Some lower bounds for the problem (previously proposed in Ref. 1) will be used in order to evaluate the quality of our method and the solutions according to the different criteria.