Abstract: Clustering is an intensive research for some years
because of its multifaceted applications, such as biology, information
retrieval, medicine, business and so on. The expectation maximization
(EM) is a kind of algorithm framework in clustering methods, one
of the ten algorithms of machine learning. Traditionally, optimization
of objective function has been the standard approach in EM. Hence,
research has investigated the utility of evolutionary computing and
related techniques in the regard. Chemical Reaction Optimization
(CRO) is a recently established method. So the property embedded
in CRO is used to solve optimization problems. This paper presents
an algorithm framework (EM-CRO) with modified CRO operators
based on EM cluster problems. The hybrid algorithm is mainly
to solve the problem of initial value sensitivity of the objective
function optimization clustering algorithm. Our experiments mainly
take the EM classic algorithm:k-means and fuzzy k-means as an
example, through the CRO algorithm to optimize its initial value, get
K-means-CRO and FKM-CRO algorithm. The experimental results
of them show that there is improved efficiency for solving objective
function optimization clustering problems.
Abstract: Chemical Reaction Optimization (CRO) is an
optimization metaheuristic inspired by the nature of chemical
reactions as a natural process of transforming the substances from
unstable to stable states. Starting with some unstable molecules with
excessive energy, a sequence of interactions takes the set to a state of
minimum energy. Researchers reported successful application of the
algorithm in solving some engineering problems, like the quadratic
assignment problem, with superior performance when compared with
other optimization algorithms. We adapted this optimization
algorithm to the Printed Circuit Board Drilling Problem (PCBDP)
towards reducing the drilling time and hence improving the PCB
manufacturing throughput. Although the PCBDP can be viewed as
instance of the popular Traveling Salesman Problem (TSP), it has
some characteristics that would require special attention to the
transactions that explore the solution landscape. Experimental test
results using the standard CROToolBox are not promising for
practically sized problems, while it could find optimal solutions for
artificial problems and small benchmarks as a proof of concept.
Abstract: Stock portfolio selection is a classic problem in finance,
and it involves deciding how to allocate an institution-s or an individual-s
wealth to a number of stocks, with certain investment objectives
(return and risk). In this paper, we adopt the classical Markowitz
mean-variance model and consider an additional common realistic
constraint, namely, the cardinality constraint. Thus, stock portfolio
optimization becomes a mixed-integer quadratic programming problem
and it is difficult to be solved by exact optimization algorithms.
Chemical Reaction Optimization (CRO), which mimics the molecular
interactions in a chemical reaction process, is a population-based
metaheuristic method. Two different types of CRO, named canonical
CRO and Super Molecule-based CRO (S-CRO), are proposed to solve
the stock portfolio selection problem. We test both canonical CRO
and S-CRO on a benchmark and compare their performance under
two criteria: Markowitz efficient frontier (Pareto frontier) and Sharpe
ratio. Computational experiments suggest that S-CRO is promising
in handling the stock portfolio optimization problem.