– Nature‐inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), for solving engineering optimization tasks., – The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature., – BA has been carefully implemented and carried out optimization for eight well‐known optimization tasks; then a comparison has been made between the proposed algorithm and other existing algorithms., – The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail.

Bat algorithm: a novel approach for global engineering optimization

Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems

A novel population-based algorithm based on the mine bomb explosion concept, called the mine blast algorithm (MBA), is applied to the constrained optimization and engineering design problems. A comprehensive comparative study has been carried out to show the performance of the MBA over other recognized optimizers in terms of computational effort (measured as the number of function evaluations) and function value (accuracy). Sixteen constrained benchmark and engineering design problems have been solved and the obtained results were compared with other well-known optimizers. The obtained results demonstrate that, the proposed MBA requires less number of function evaluations and in most cases gives better results compared to other considered algorithms.

Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems

We propose a novel hybrid algorithm named PSO-DE, which integrates particle swarm optimization (PSO) with differential evolution (DE) to solve constrained numerical and engineering optimization problems. Traditional PSO is easy to fall into stagnation when no particle discovers a position that is better than its previous best position for several generations. DE is incorporated into update the previous best positions of particles to force PSO jump out of stagnation, because of its strong searching ability. The hybrid algorithm speeds up the convergence and improves the algorithm's performance. We test the presented method on 11 well-known benchmark test functions and five engineering optimization functions. Comparisons show that PSO-DE outperforms or performs similarly to seven state-of-the-art approaches in terms of the quality of the resulting solutions.

Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization

Technical and commercial progress in the adoption of geopolymer cement

The development of a changing range genetic algorithm

The development of a simulation model of the dense packing of large particulate assemblies

Application of genetic algorithm for modeling of dense packing of concrete aggregates

The optimization of aggregate blends for sustainable low cement concrete

A simulation algorithm was developed for modeling the dense packing of large assemblies of particulate materials (in the order of millions). These assemblies represent the real aggregate systems of portland cement concrete. Two variations of the algorithm are proposed: sequential packing model and particle suspension model. A developed multicell packing procedure as well as fine adjustment of the algorithm's parameters were useful to optimize the computational resources (i.e., to realize the trade-off between the memory and packing time). Some options to speed up the algorithm and to pack very large volumes of spherical entities (up to 10 million) are discussed. The described procedure resulted in a quick method for packing of large assemblies of particulate materials. The influence of model variables on the degree of packing and the corresponding distribution of particles was analyzed. Based on the simulation results, different particle size distributions of particulate materials are correlated to their ...

Adil Amirjanov

Papers

The development of a changing range genetic algorithm

The development of a simulation model of the dense packing of large particulate assemblies

Application of genetic algorithm for modeling of dense packing of concrete aggregates

The optimization of aggregate blends for sustainable low cement concrete

Optimization of a Computer Simulation Model for Packing of Concrete Aggregates