Applied Metaheuristic Computing: 2nd Volume

Dear Colleagues,

For decades, applied metaheuristic computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, and facility layout planning, among others. This is partly because classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, by contrast, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. The most commonly used AMC methods include:

• Ant colony optimization;
• Differential evolution;
• Evolutionary computation;
• Genetic algorithm;
• GRASP;
• Hyper-heuristics;
• Memetic algorithm;
• Particle swarm optimization;
• Scatter search;
• Simulated annealing;
• Tabu search;
• Variable neighborhood search.

I encourage the submission of your best papers within the topic of AMC.

Prof. Dr. Peng-Yeng Yin
Prof. Dr. Ray-I Chang
Prof. Dr. Jen-Chun Lee
Topic Editors