Dynamic and stochastic models for the allocation of empty containers
Operations Research
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An Immunological Approach to Combinatorial Optimization Problems
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
A New GA Approach for the Vehicle Routing Problem
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
IEEE Transactions on Parallel and Distributed Systems
Human evolutionary model: A new approach to optimization
Information Sciences: an International Journal
Immune optimization algorithm for constrained nonlinear multiobjective optimization problems
Applied Soft Computing
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
A novel immune inspired approach to fault detection
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Recognition of handwritten indic script using clonal selection algorithm
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
A population adaptive based immune algorithm for solving multi-objective optimization problems
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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The development of evolutionary algorithms for optimization has always been a stimulating and growing research area with an increasing demand in using them to solve complex industrial optimization problems. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrained multi-objective optimization problems is developed in this research. The algorithm adopts the clonal selection and immune suppression theories, with a sorting scheme featuring uniform crossover, multi-point mutation, non-dominance and crowding distance sorting to attain the Pareto optimal front in an efficient manner. The proposed algorithm was verified with nine benchmarking functions on its global optimal search ability as well as compared with four optimization algorithms to assess its diversity and spread. Sensitivity analysis was also carried out to investigate the selection of key parameters of the algorithm. It is found that the developed immunity-based hybrid evolutionary algorithm provides a useful means for solving optimization problems and has successfully applied to the problem of global repositioning of containers, which is one of a constrained multi-objective optimization problem. The developed HAIS will assist shipping liners on timely decision making and planning of container repositioning operations in global container transportation business in an optimized and cost effective manner.