Ali Fuat Alkaya

Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem

We propose a new nature inspired metaheuristic approach based on the V flight formation of the migrating birds which is proven to be an effective formation in energy saving. Its performance is tested on quadratic assignment problem instances arising from a real life problem and very good results are obtained. The quality of the solutions we report are better than simulated annealing, tabu search, genetic algorithm, scatter search, particle swarm optimization, differential evolution and guided evolutionary simulated annealing approaches. The proposed method is also tested on a number of benchmark problems obtained from the QAPLIB and in most cases it was able to obtain the best known solutions. These results indicate that our new metaheuristic approach could be an important player in metaheuristic based optimization.

Scheduling continuous aluminium casting lines

This study considers the problem of scheduling casting lines of an aluminium casting and processing plant. In aluminium processing plants, continuous casting lines are the bottleneck resources, i.e. factory throughput is limited by the amount of aluminium that can be cast. The throughput of a casting line might be increased by minimizing total setup time between jobs. The objective is to minimize setup time on production lines for a given time period while balancing workload between production lines to accommodate potential new orders. A mathematical formulation for scheduling jobs to minimize the total setup time while achieving workload balance between the production lines is presented. Since the casting scheduling problem is an NP-hard problem, even with only one casting line, a four-step algorithm to find good solutions in a reasonable amount of time is proposed. In this process, a set of asymmetric travelling salesman problems is followed by a pairwise exchange heuristic. The proposed procedure is applied to a case study using real casting data.

Combining and solving sequence dependent traveling salesman and quadratic assignment problems in PCB assembly

a b s t r a c t In this study we undertake the optimization of chip shooter component placement ma- chines which became popular in assembling printed circuit boards (PCB) in recent years. A PCB is usually a rectangular plastic board on which the electrical circuit to be used in a particular electronic equipment is printed. The overall optimization of the chip shooter placement machines leads to a very complicated optimization problem which we formu- late here for the first time (without any simplifying assumptions). However, it is possible to decompose the problem into placement sequencing problem and feeder configuration problem which turn out to be sequence dependent traveling salesman problem (SDTSP) and Quadratic Assignment Problem (QAP), respectively. We use simulated annealing meta- heuristic approach and the heuristics developed for the SDTSP in an earlier study to solve these two problems in an iterative manner. We also attempt to solve the combined over- all optimization problem by simulated annealing and artificial bee colony metaheuristics and compare their performances with the iterative approach. The results are in favor of iterative approach.

Performance of migrating birds optimization algorithm on continuous functions

In this study, we evaluate the performance of a recently proposed metaheuristic on several well-known functions. The objective of this evaluation is to participate in a competition where several metaheuristics are compared. The metaheuristic we exploit is the recently proposed migrating birds optimization (MBO) algorithm. Our contribution in this study is to develop a novel neighbor generating function for MBO to be used in multidimensional continuous spaces. After a set of preliminary tests presenting the best performing values of the parameters, the results of computational experiments are given in 2, 10 and 30 dimensions.

Metaheuristic based solution approaches for the obstacle neutralization problem

We consider the obstacle neutralization problem.We exploit metaheuristics to solve this problem.We also provide comprehensive computational experiments involving both real and synthetic naval minefield data. The problem of finding shortest path under certain constraints is NP-Complete except for some trivial variants. In this study, we develop metaheuristics for the obstacle neutralization problem (ONP) which is a path planning problem where the goal is to safely and swiftly navigate an agent from a given source location to a destination through an arrangement of potential mine or threat discs in the plane. To solve the ONP, ant system, genetic algorithm, simulated annealing and migrating birds optimization algorithms are developed and customized. We provide computational experiments both on real-world and synthetic data to empirically assess their performance. The results of the algorithms are compared with exact solutions on small instances. The comparison results present that our algorithms finds near-optimal solutions in reasonable execution times. Furthermore, the results show that the proposed versions of the aforementioned algorithms can be applicable to similar problems.

Migrating birds optimization: a new meta-heuristic approach and its application to the quadratic assignment problem

In this study we propose a new nature inspired metaheuristic approach based on the V formation flight of the migrating birds which is proven to be an effective formation in energy minimization. Its performance is tested on quadratic assignment problem instances arising from a real life problem and very good results are obtained. The quality of the solutions turned out to be better than simulated annealing, tabu search and guided evolutionary simulated annealing approaches. These results indicate that our new metaheuristic approach could be an important player in metaheuristic based optimization.

A task scheduling algorithm for arbitrarily-connected processors with awareness of link contention

In this paper, we present a new task scheduling algorithm, called Contention-Aware Scheduling (CAS) algorithm, with the objective of delivering good quality of schedules in low running-time by considering contention on links of arbitrarily-connected, heterogeneous processors. The CAS algorithm schedules tasks on processors and messages on links by considering the earliest finish time attribute with the virtual cut-through (VCT) or the store-and-forward (SAF) switching. There are three types of CAS algorithm presented in this paper, which differ in ordering the messages from immediate predecessor tasks. As part of the experimental study, the performance of the CAS algorithm is compared with two well-known APN (arbitrary processor network) scheduling algorithms. Experiments on the results of the synthetic benchmarks and the task graphs of the well-known problems clearly show that our CAS algorithm outperforms the related work with respect to performance (given in normalized schedule length) and cost (given in running time) to generate output schedules.

Application of Sequence-Dependent Traveling Salesman Problem in Printed Circuit Board Assembly

Optimization issues regarding the automated assembly of printed circuit boards attracted the interest of researchers for several decades. This is because even small gains in assembly time result in very important benefits in mass production. In this paper, the focus is on a particular placement machine type that has a rotational turret and a stationary component magazine. So far, this type of machine received little attention among the researchers. In this paper, the feeder configuration, placement sequencing, and assembly time minimization problems are formulated explicitly and completely (without simplifying assumptions) using nonlinear integer programming. In addition, the placement sequencing problem is shown to be a recently introduced new generalization of the traveling salesman problem (the sequence-dependent traveling salesman). These formulations show the complexity of the problems and the need for effective heuristic designs for solving them. We propose three heuristics that improve previously suggested solution methods and give comparable results when compared to simulated annealing that is a widely accepted good performing metaheuristic on combinatorial optimization problems. The heuristics are experimentally shown to improve previous methods significantly in assembly time that implies a huge economic benefit. The heuristics proposed could also be applied to other placement machines with similar operation principles.

A penalty search algorithm for the obstacle neutralization problem

We consider a path planning problem wherein an agent needs to swiftly navigate from a source to a destination through an arrangement of obstacles in the plane. We suppose the agent has a limited neutralization capability in the sense that it can safely pass through an obstacle upon neutralization at a cost added to the traversal length. The agents goal is to find the sequence of obstacles to be neutralized en route that minimizes the overall traversal length subject to the neutralization limit. We call this problem the obstacle neutralization problem (ONP), which is essentially a variant of the intractable weight-constrained shortest path problem in the literature. In this study, we propose a simple, yet efficient and effective suboptimal algorithm for ONP based on the idea of penalty search and we present special cases where our algorithm is provably optimal. Computational experiments involving both real and synthetic naval minefield data are also presented.

An ant system algorithm for the neutralization problem

We consider a path planning problem wherein an agent needs to safely and swiftly navigate from a given source location to a destination through an arrangement of disk-shaped obstacles. The agent possesses a limited neutralization capability in the sense that it can neutralize a certain number of obstacles enroute and pass through them safely upon neutralization. Optimal utilization of such a capability is called the neutralization problem. This problem is essentially a shortest path problem with resource constraints, which has been shown to be NP-Hard except for some trivial variants. In this study, we propose an ant system algorithm for the neutralization problem. In the proposed algorithm, the state transition rule makes use of certain problem-specific information to guide the ants. We show how the parameters of the algorithm can be fine-tuned for enhanced performance and we present limited computational experiments including a real-world naval minefield dataset. Our experiments suggest that the proposed algorithm finds high quality solutions in general with reasonable computational resources.