Scope of stationary multi-objective evolutionary optimization: a case study on a hydro-thermal power dispatch problem |
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Authors: | Kalyanmoy Deb |
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Institution: | (1) Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India |
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Abstract: | Many engineering design and developmental activities finally resort to an optimization task which must be solved to get an
efficient and often an intelligent solution. Due to various complexities involved with objective functions, constraints, and
decision variables, optimization problems are often not adequately suitable to be solved using classical point-by-point methodologies.
Evolutionary optimization procedures use a population of solutions and stochastic update operators in an iteration in a manner
so as to constitute a flexible search procedure thereby demonstrating promise to such difficult and practical problem-solving
tasks. In this paper, we illustrate the power of evolutionary optimization algorithms in handling different kinds of optimization
tasks on a hydro-thermal power dispatch optimization problem: (i) dealing with non-linear, non-differentiable objectives and
constraints, (ii) dealing with more than one objectives and constraints, (iii) dealing with uncertainties in decision variables
and other problem parameters, and (iv) dealing with a large number (more than 1,000) variables. The results on the static
power dispatch optimization problem are compared with that reported in an existing simulated annealing based optimization
procedure on a 24-variable version of the problem and new solutions are found to dominate the solutions of the existing study. Importantly, solutions found by our approach are found to satisfy theoretical Kuhn–Tucker
optimality conditions by using the subdifferentials to handle non-differentiable objectives. This systematic and detail study
demonstrates that evolutionary optimization procedures are not only flexible and scalable to large-scale optimization problems,
but are also potentially efficient in finding theoretical optimal solutions for difficult real-world optimization problems.
Kalyanmoy Deb, Deva Raj Chair Professor.
Currently a Finland Distinguished Professor, Department of Business Technology, Helsinki School of Economics, 00101 Helsinki,
Finland. |
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Keywords: | Multi-objective optimization Kuhn– Tucker conditions Evolutionary optimization Robust optimization Large-scale optimization |
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