Antony W. Iorio

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This paper demonstrates that the self-adaptive technique of Differential Evolution (DE) can be simply used for solving a multiobjective optimization problem where parameters are interdependent. The real-coded crossover and mutation rates within the NSGA-II have been replaced with a simple Differential Evolution scheme, and results are reported on a rotated(More)
The following paper describes a cooperative coevolutionary algorithm which incorporates a novel collaboration formation mechanism. It encourages rewarding of components participating in successful collaborations from each sub-population. The successfulness of the collaboration is measured by a non-dominated sorting procedure. The algorithm has demonstrated(More)
Multi-objective problems with parameter interactions can present difficulties to many optimization algorithms. We have investigated the behaviour of Simplex Crossover (SPX), Unimodal Normally Distributed Crossover (UNDX), Parent-centric Crossover (PCX), and Differential Evolution (DE), as possible alternatives to the Simulated Binary Crossover (SBX)(More)
The field of Differential Evolution (DE) has demonstrated important advantages in single objective optimization. To date, no previous research has explored how the unique characteristics of DE can be applied to multi-objective optimization. This paper explains and demonstrates how DE can provide advantages in multi-objective optimization using directional(More)
Differential Evolution (DE) is a powerful optimization procedure that self-adapts to the search space, although DE lacks diversity and sufficient bias in the mutation step to make efficient progress on non-separable problems. We present an enhancement to DE that introduces greater diversity while also directing the search to more promising regions. The(More)
Being able to learn the synchronization behavior of a networked complex system has profound implications for studying and modeling many natural and artificial phenomena, such as the spread of diseases, emergence of social trends, as well as more effective agent based distillation models. In order to study the practicality of learning synchronization(More)
In decision making a key source of uncertainty is people’s perception of information which is influenced by their attitudes toward risk. Both, perception of information and risk attitude, affect the interpretation of information and hence the choice of suitable courses of action in a variety of contexts ranging from project planning to military operations.(More)
This paper presents four rotatable multi-objective test problems that are designed for testing EMO (Evolutionary Multi-objective Optimization) algorithms on their ability in dealing with parameter interactions. Such problems can be solved efficiently only through simultaneous improvements to each decision variable. Evaluation of EMO algorithms with respect(More)