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Sine cosine algorithm (SCA) is one of the most recent population-based optimization algorithms proposed for solving optimization problems. In the present study, in order to improve the performance of this algorithm, a new weighted update position mechanism (WUPM) was employed instead of the position update method of search agents in SCA. In the proposed method, in addition to a position and fitness,...
This paper aims to introduce an algorithm by performing three modifications in the GCS algorithm in order to enhance the rate of convergence using globally best solutions. The Cuckoo Search Algorithm, CSA, which has developed recently, is an optimization method that uses random equations for search engine and therefore the rate of convergence is considerably low. The improved algorithm developed,...
This paper proposed a novel bio-inspired optimizer, namely the root system growth algorithm (RSGA), which adopts the root foraging, memory and communication, and auxin-regulated mechanisms of the root system. When tested against benchmark functions, the RSGA markedly outperforms the CMA-ES, PSO, GA, and DE algorithms in terms of accuracy, robustness and convergence speed.
A novel Ant inspired method is introduced in which both positive and negative pheromones are used to guide the ant's selection process. The negative pheromone serves to influence the decision (much like a tabu search) to discourage the exploration of known bad paths. The positive pheromone serves to attract ants to known good paths (as in any conventional ACO.) Psuedocode for the new algorithm is...
Particle swarm optimization can be viewed as a system with two populations: a population of current positions and a population of personal best attractors. In genetic algorithms, crossover is applied after selection - the goal is to create a new offspring solution using components from the best available solutions. In a particle swarm, the best available solutions are in the population of personal...
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