Eugene Y. C. Wong

Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning

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.

Immunity-based evolutionary algorithm for optimal global container repositioning in liner shipping

Global container repositioning in liner shipping has always been a challenging problem in container transportation as the global market in maritime logistics is complex and competitive. Supply and demand are dynamic under the ever changing trade imbalance. A useful computation optimization tool to assist shipping liners on decision making and planning to reposition large quantities of empty containers from surplus countries to deficit regions in a cost effective manner is crucial. A novel immunity-based evolutionary algorithm known as immunity-based evolutionary algorithm (IMEA) is developed to solve the multi-objective container repositioning problems in this research. The algorithm adopts the clonal selection and immune suppression theories to attain the Pareto optimal front. The proposed algorithm was verified with benchmarking functions and compared with four optimization algorithms to assess its diversity and spread. The developed algorithm provides a useful means to solve the problem and assist shipping liners in the global container transportation operations in an optimized and cost effective manner.

Advancement in the twentieth century in artificial immune systems for optimization: Review and future outlook

Research in Artificial Immune Systems (AIS) for optimization has attracted attention in recent years. Exploration and adoption of the inspired immune theories in clonal selection, immune network, negative selection, and danger signaling is becoming a popular basis for algorithm design for solving optimization problems, especially on multi-objective optimizations. Novel algorithms are design, benchmarked and applied to real life applications. This paper aims to review and outlook on the latest development of AIS-based algorithms in the recent decade. An analysis of the AIS applications is also discussed.

An AIS-based dynamic routing (AISDR) framework

An Artificial Immune System-based Dynamic Routing (AISDR) framework is engineered through the adoption of the characteristics that are analogous to human immune system for solving dynamic routing problems. The framework covers the profound features on recognition, selection, learning, memory, and adaptation capabilities. An AISDR algorithm is developed that incorporates the features of clonal selection, affinity maturation, and immunological memory features. Simulation study is carried out to evaluate the performance of the algorithm in the global shipment operation.

Assessing factors in mobile marketing context model adopting TAM, Commitment-Trust Theory, environment and emotional items in facilitating purchasing intention

Mobile marketing is a relatively easy and inexpensive way to promote products. This paper evaluates influential factors that motivate consumers in responding to mobile marketing with the consideration of context receiving time and location. A mobile marketing context model, with Technology acceptance model (TAM) and Commitment-trust Theory being adopted, is developed to facilitate purchasing intention in retails. A number of hypotheses were formulated on the effect of time and location of consumer attitudes towards mobile context. A marketing survey is conducted with one hundred and twenty effective respondents received. The empirical findings concluded with a positive relationship among trust and commitments in attitudes, perceived usefulness, ease of use and purchase intention defined under time and location scenarios. Future development on developing a regression analysis and linear model as well as assessing factors of gender and level convenient towards purchasing intention could be explored.

Immunity-based hybrid evolutionary algorithm for multi-objective optimization

The development of evolutionary algorithms for optimization has been a growing research area. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrainedmulti-objectiveoptimization problemsis developed. 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 Pareto optimal in an efficient manner. The algorithm was verified with nine benchmarking functions on its global optimal search ability and compared with four optimization algorithms to assess its diversity and spread. It is found that the immunity-based algorithm provides a useful means for solving optimization problems, and has proved its capability in global optimal search in multi-objective optimization.