Abstract
As a novel evolutionary optimization method, extremal optimization (EO) has been successfully applied to a variety of combinatorial optimization problems. However, the applications of EO in continuous optimization problems are relatively rare. This paper proposes an improved real-coded population-based EO method (IRPEO) for continuous unconstrained optimization problems. The key operations of IRPEO include generation of real-coded random initial population, evaluation of individual and population fitness, selection of bad elements according to power-law probability distribution, generation of new population based on uniform random mutation, and updating the population by accepting the new population unconditionally. The experimental results on 10 benchmark test functions with the dimensionN=30have shown that IRPEO is competitive or even better than the recently reported various genetic algorithm (GA) versions with different mutation operations in terms of simplicity, effectiveness, and efficiency. Furthermore, the superiority of IRPEO to other evolutionary algorithms such as original population-based EO, particle swarm optimization (PSO), and the hybrid PSO-EO is also demonstrated by the experimental results on some benchmark functions.
Highlights
It has been widely recognized that a variety of real-world complex engineering optimization problems can be formulated as continuous unconstrained optimization problems [1]
The key operations of improved real-coded population-based EO method (IRPEO) include generation of real-coded random initial population, evaluation of individual and population fitness, selection of bad elements according to power-law probability distribution, generation of new population based on uniform random mutation, and updating the population by accepting the new population unconditionally
This paper focuses on another novel evolutionary algorithm called extremal optimization (EO) for continuous unconstrained optimization problems
Summary
It has been widely recognized that a variety of real-world complex engineering optimization problems can be formulated as continuous unconstrained optimization problems [1]. The effectiveness of PSO-EO has been demonstrated by the experimental results on 6 benchmark test functions Another similar hybrid algorithm for continuous problem is based on improved shuffled frog-leaping algorithm and EO [23]. Following this line of PEO, this paper extends the basic idea of PEO to continuous unconstrained optimization problems and presents an improved real-coded populationbased EO (IRPEO) algorithm. The superiority of IRPEO to various GA [24] algorithms with different mutation operations is demonstrated by the experimental results on 10 continuous unconstrained optimization benchmark test functions. The experimental results on these benchmark functions have shown that the proposed IRPEO provides better performance than other evolutionary algorithms such as PSO, original population-based EO, and the hybrid PSOEO algorithm [22].
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