1 research outputs found
Hybrid Evolutionary Computation for Continuous Optimization
Hybrid optimization algorithms have gained popularity as it has become
apparent there cannot be a universal optimization strategy which is globally
more beneficial than any other. Despite their popularity, hybridization
frameworks require more detailed categorization regarding: the nature of the
problem domain, the constituent algorithms, the coupling schema and the
intended area of application. This report proposes a hybrid algorithm for
solving small to large-scale continuous global optimization problems. It
comprises evolutionary computation (EC) algorithms and a sequential quadratic
programming (SQP) algorithm; combined in a collaborative portfolio. The SQP is
a gradient based local search method. To optimize the individual contributions
of the EC and SQP algorithms for the overall success of the proposed hybrid
system, improvements were made in key features of these algorithms. The report
proposes enhancements in: i) the evolutionary algorithm, ii) a new convergence
detection mechanism was proposed; and iii) in the methods for evaluating the
search directions and step sizes for the SQP local search algorithm. The
proposed hybrid design aim was to ensure that the two algorithms complement
each other by exploring and exploiting the problem search space. Preliminary
results justify that an adept hybridization of evolutionary algorithms with a
suitable local search method, could yield a robust and efficient means of
solving wide range of global optimization problems. Finally, a discussion of
the outcomes of the initial investigation and a review of the associated
challenges and inherent limitations of the proposed method is presented to
complete the investigation. The report highlights extensive research,
particularly, some potential case studies and application areas.Comment: Companion Publications for this Technical Memorandum, available at
IEEE Xplore, are: [1] H. A. Bashir and R. S. Neville, "Convergence
measurement in evolutionary computation using Price's theorem," IEEE (CEC),
2012. [2] H. A. Bashir and R. S. Neville, "A hybrid evolutionary computation
algorithm for global optimization," IEEE (CEC), 201