295 research outputs found
Feasibility and dominance rules in the electromagnetism-like algorithm for constrained global optimization
This paper presents the use of a constraint-handling technique, known as feasibility and dominance rules, in a electromagnetismlike
(ELM) mechanism for solving constrained global optimization problems. Since the original ELM algorithm is specifically designed for solving bound constrained problems, only the inequality and equality constraints violation together with the objective function value are used to select
points and to progress towards feasibility and optimality. Numerical experiments are presented, including a comparison with other methods recently reported in the literature
Constrained power plants unit loading optimization algorithm
Power plants unit loading optimization problem is of practical importance in the power industry. It generally involves minimizing the total operating cost subject to satisfy a series of constraints. Minimizing fuel consumption while achieve output demand and maintain emissions within the environmental license limits is a major objective for the loading optimization. This paper presents a Particle Swarm Optimization (PSO) based approach for economically dispatching generation load among different generators based on the units' performance. Constraints have been handled by a proposed modified PSO algorithm which adopting preserving feasibility and repairing infeasibility strategies. A simulation of an Australia power plant implementing the modified algorithm is reported. The result reveals the capability, effectiveness and efficiency of using evolutionary algorithms such as PSO in solving significant industrial problems in the power industry
Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review
Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COP
Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems
In this paper, we present a new stochastic hybrid technique for constrained global optimization. It is a combination of the electromagnetism-like (EM) mechanism with a random local search, which is a
derivative-free procedure with high ability of producing a descent direction. Since the original EM algorithm is specifically designed for solving bound constrained problems, the approach herein adopted for handling
the inequality constraints of the problem relies on selective conditions that impose a sufficient reduction either in the constraints violation or in the objective function value, when comparing two points at a time.
The hybrid EM method is tested on a set of benchmark engineering design problems and the numerical results demonstrate the effectiveness of the proposed approach. A comparison with results from other
stochastic methods is also included
Hybrid optimization coupling electromagnetism and descent search for engineering problems
In this paper, we present a new stochastic hybrid technique for constrained
global optimization. It is a combination of the electromagnetism-like (EM) mechanism
with an approximate descent search, which is a derivative-free procedure with
high ability of producing a descent direction. Since the original EM algorithm is
specifically designed for solving bound constrained problems, the approach herein
adopted for handling the constraints of the problem relies on a simple heuristic
denoted by feasibility and dominance rules. The hybrid EM method is tested on
four well-known engineering design problems and the numerical results demonstrate
the effectiveness of the proposed approach
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