This paper introduces an adaptive species conservation genetic algorithm (ASCGA) by defining a species with three parameters: species seed, species radius and species boundary fitness. A species is defined as a group ...
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This paper introduces an adaptive species conservation genetic algorithm (ASCGA) by defining a species with three parameters: species seed, species radius and species boundary fitness. A species is defined as a group of individuals that have similar characteristics and that are dominated by the best individual in the species, called the species seed. Species radius defines the species' upper boundary and the species boundary fitness is the lowest value of fitness in the boundary. Some heuristic algorithms have been developed to adjust these parameters and an ASCGA has been proposed to solve multimodal optimization problems. With heuristic techniques, ASCGA can automatically adjust species parameters and allow the species to adapt to an optimization problem. Experimental results presented demonstrate that the proposed algorithm is capable of finding the global and local optima of test multimodal optimization problems with a higher efficiency than the methods from the literature. ASCGA has also successfully found a significantly different solution of a 25-bar space truss design and identified 761 local solutions of the 2-D Shubert function. Copyright (C) 2009 John Wiley & Sons, Ltd.
Due to the challenging constraint search space of real-world engineering problems, a variation of the Chimp Optimization Algorithm (ChOA) called the Universal Learning Chimp Optimization Algorithm (ULChOA) is proposed...
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Due to the challenging constraint search space of real-world engineering problems, a variation of the Chimp Optimization Algorithm (ChOA) called the Universal Learning Chimp Optimization Algorithm (ULChOA) is proposed in this paper, in which a unique learning method is applied to all previous best knowledge obtained by chimps (candid solutions) to update prey's positions (best solution). This technique preserves the chimp's variety, discouraging early convergence in multimodal optimization problems. Furthermore, ULChOA introduces a unique constraint management approach for dealing with the constraints in real-world constrained optimization issues. A total of fifteen commonly recognized multimodal functions, twelve real-world constrained optimization challenges, and ten IEEE CEC06-2019 suit tests are utilized to assess the ULChOA's performance. The results suggest that the ULChOA surpasses sixteen out of eighteen algorithms by an average Friedman rank of better than 78 percent for all 25 numerical functions and 12 engineering problems while outperforming jDE100 and DISHchainle + 12 by 21% and 39%, respectively. According to Bonferroni-Dunn and Holm's tests, ULChOA is statistically superior to benchmark algorithms regarding test functions and engineering challenges. We believe that the ULChOA proposed here may be utilized to solve challenges requiring multimodal search spaces. Furthermore, ULChOA is more widely applicable to engineering applications than competitor benchmark algorithms.
Particle Swarm Optimization(PSO) is a metaheuristic optimization algorithm that have been used to solve complex optimization problems that the traditional techniques finds very difficult to solve. The Interior-Point M...
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Particle Swarm Optimization(PSO) is a metaheuristic optimization algorithm that have been used to solve complex optimization problems that the traditional techniques finds very difficult to solve. The Interior-Point Methods(IPMs) are efficient tools for solving nonlinear optimization problems. The IPMs having constrains that are active at the current point, are now believed to be the most robust algorithms for solving large-scale nonlinear optimization problems. Though they are very efficient, but they are still plagued with several challenges such as how to handle of nonconvexity, the procedure for making the barrier constraint up to date is cumbersome despite the existence of nonlinearities, and the need to ensure progress toward the solution. In order to overcome some of the shortcomings of the standard PSO such as premature convergence and particles been trapped at the local minimal, we proposed the Primal-Dual Interior Point Particle Swarm Optimization(pdipm PSO) to surmount the shortcomings of the original PSO. We applied the Primal Dual to each particle in a finite number of iterations, and feed the PSO with the output of the Primal Dual. We compared the performance of our new algorithm(pdipmPSO) with IPM and PSO using 13 different benchmark functions. Optimization results reveal that pdipm PSO performs better than PSO and IPM. Our proposed algorithm is shown to have great capacity to prevent premature convergence, and the curse of particles being trapped in the local minimal which have characterised many variants of PSO.
Metaheuristics have recently been commonly used to solve complex problems in real applications. Some scholars used multiple populations in metaheuristic approaches to shorten the execution time in finding nearly optim...
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Metaheuristics have recently been commonly used to solve complex problems in real applications. Some scholars used multiple populations in metaheuristic approaches to shorten the execution time in finding nearly optimal solutions. In this paper, we revisit the properties of sub-population execution for genetic algorithm (GA) and design a level-wise hierarchical sub-population architecture. We also make experiments for comparing the performance of the architecture for unimodal and multimodal functions. The experimental results show that using the hierarchical GA architecture produces a more significant improvement on multimodal functions than on unimodal ones.
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