Sang-Moon Soak

A New Encoding for the Degree Constrained Minimum Spanning Tree Problem

We present an effective new encoding method for use by black-box optimisation methods when addressing tree-based combinatorial problems. It is simple, easily handles degree constraints, and is easily extendable to incorporate problem-specific knowledge. We test it on published benchmark degree-constrained minimum spanning tree (DC-MST) problems, comparing against two other well-known encodings. The new method outperforms the comparative encodings. We have not yet compared against the recently published ‘edge-sets’ encoding, however we can report preliminary work which indicates sophisticated versions of the new encoding can outperform edge-set on at least some classes of DC-MST.

A New Evolutionary Algorithm for Spanning-Tree Based Communication Network Design

A novel evolutionary algorithm is described for designing the topology of spanning tree-based communication networks. Two specific performance objectives are dealt with: the optimum communication spanning tree problem (OCSTP), and the quadratic minimum spanning tree problem (q-MST). Improved network performance is reliably obtained when using the proposed algorithm on accepted benchmark instances, in comparison with the previous best-known approaches. The same methodology can be applied straightforwardly to the design of communication networks with other objectives.

New Genetic Crossover Operator for the TSP

Genetic algorithm is very useful method for global search of large search space and has been applied to various problems. It has two kinds of important search mechanisms, crossover and mutation. Especially many researchers have more interested in crossover operator than mutation operator because crossover operator has charge of the responsibility of local search. In this paper we introduce a new crossover operator avoiding the drawback of conventional crossovers. We compare it to several crossover operators for travelling salesman problem (TSP) for showing the performance of proposed crossover.

The property analysis of evolutionary algorithms applied to spanning tree problems

The search behavior of an evolutionary algorithm depends on the interactions between the encoding that represents candidate solutions to the target problem and the operators that act on that encoding. In this paper, we focus on analyzing some properties such as locality, heritability, population diversity and searching behavior of various decoder-based evolutionary algorithm (EA) frameworks using different encodings, decoders and genetic operators for spanning tree based optimization problems. Although debate still continues on how and why EAs work well, many researchers have observed that EAs perform well when its encoding and operators exhibit good locality, heritability and diversity properties.We analyze these properties of various EA frameworks with two types of analytical ways on different spanning tree problems; static analysis and dynamic analysis, and then visualize them. We also show through this analysis that EA using the Edge Set encoding (ES) and the Edge Window Decoder encoding (EWD) indicate very good locality and heritability as well as very good diversity property. These are put forward as a potential explanation for the recent finding that they can outperform other recent high-performance encodings on the constrained spanning tree problems.

The improved adaptive link adjustment evolutionary algorithm for the multiple container packing problem

In this paper, we propose a new version of Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA) for the network optimization problems, and apply it to the multiple container packing problem (MCPP). Because the proposed algorithm uses a different encoding method from that of the original ALA-EA, we also need different decoding methods for the new algorithm. In addition, to improve the performance of the proposed algorithm, we incorporate heuristic local improvement approaches into it. To verify the effectiveness of the proposed algorithm we compare it with the existing evolutionary approaches for several instances, which are known to be extremely difficult to them. Computational tests show that the algorithm is superior to the existing evolutionary approaches and the original ALA-EA in both of the solution quality and the computational time. Moreover, the performance seems to be not affected by an instance property.

New subtour-based crossover operator for the TSP

Genetic algorithm (GA) is a very useful method for the global search of large search space and has been applied to various problems. It has two kinds of important search mechanisms, crossover and mutation. Because the performance of GA depends on these operators, a large number of operators have been developed for improving the performance of GA. Especially many researchers have more interested in crossover operator than mutation operator because crossover operator has charge of the responsibility of local search. We only deal with crossover operator.

Meta-heuristic Method for the Single Source Capacitated Facility Location Problem

The facility location problem is one of the traditional optimization problems. In this paper, we deal with the single source capacitated facility location problem (SSCFLP) and it is known as an NP-hard problem. Thus, it seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the SSCFLP. This paper introduces a new efficient evolutionary algorithm for the SSCFLP. The proposed algorithm is devised by incorporating a general adaptive link adjustment evolutionary algorithm and three heuristic local search methods. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds optimum solutions at almost all middle size test instances and very stable solutions at larger size test instances.