Comparison Of Metaheuristic Algorithms Research Articles

Chance-Constrained Optimization Formulation for Ship Conceptual Design: A Comparison of Metaheuristic Algorithms

This paper presents a new chance-constrained optimization (CCO) formulation for the bulk carrier conceptual design. The CCO problem is modeled through the scenario design approach. We conducted extensive numerical experiments comparing the convergence of both canonical and state-of-the-art metaheuristic algorithms on the original and CCO formulations and showed that the CCO formulation is substantially more difficult to solve. The two best-performing methods were both found to be differential evolution-based algorithms. We then provide an analysis of the resulting solutions in terms of the dependence of the distribution functions of the unit transportation costs and annual cargo capacity of the ship design on the probability of violating the chance constraints.

A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms

This study proposes a scenario-based mixed-integer programming model to investigate the responsive-resilient inventory-location problem under uncertainty. The proposed model minimizes the total costs and makes decisions about the location, allocation, and inventory problems. The literature showed that simultaneous consideration of responsiveness and resilience measures has been ignored by the researchers. Hence, to fill this gap, this study considers responsiveness and resilience measures in the proposed model. Also, since the uncertainty exists in the nature of the research problem due to changes in the business environment, this paper applies the queuing theory and robust fuzzy stochastic optimization to cope with uncertainty. At first, the existed uncertainty in lead time and demand is tackled by employing the queuing theory, and some performance measures of the system are calculated. Then, the achieved results are incorporated into the fuzzy robust stochastic model. As the problem is an NP-Hard, this study develops several metaheuristic algorithms to solve the proposed model in a reasonable time. Then, the applicability of the proposed model and efficiency of the developed algorithms are shown by several numerical examples. Eventually, several sensitivity analyses are conducted on some important parameters of the model, and useful managerial insights are provided.

Comparison of meta-heuristic algorithms in optimum operation of a single-reservoir dam system

Multi-objective models must be built to optimise the use of water supplies. In this study, the performance of the farmland fertility algorithm (FFA) and that of the Harris hawks optimisation (HHO) algorithm were evaluated and compared with each other to solve the two-objective problem of optimal operation of the Golestan dam reservoir under baseline (April 2006 to October 2018) and climate change conditions (April 2021 to October 2033) in Golestan Province, Iran. The operation rules were extracted by minimising vulnerability and maximising reliability indices under both conditions. The results showed that when considering a constant value of the reliability index (i.e. 80%), the rate of vulnerability changes in the multi-objective FFA (Moffa) is lower than that of the multi-objective HHO (Mohho) algorithm. Therefore, Moffa has performed better than the Mohho algorithm in the optimal operation of the Golestan dam reservoir under both baseline and climate change conditions. On the other hand, the comparison of the optimum rules obtained under the baseline and climate change conditions showed that supplying the water demands would be more concordant with the optimum rules under climate change conditions in both optimisation algorithms. Therefore, the Golestan dam reservoir could perform better under climate change conditions in the case of using both the mentioned algorithms.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Wind Power Optimization: A Comparison of Meta-Heuristic Algorithms

The wind being a most promising renewable energy, has become a strong contender for fossil fuels. Optimizing the blade pitch angle of a wind turbine is important to obtain the maximum power output, as the other variables are considered to be uncontrollable. In this paper an effort has been made to compare performances of three different optimization algorithms namely Particle swarm optimization (PSO), Artificial bee colony (ABC) and cuckoo search (CS) for optimizing the blade pitch angle and hence optimize the power output of a 1.5 MW capacity, pitch regulated, three-bladed horizontal axis wind turbine operating at a large wind farm in central dry zone of Karnataka. The objective function development is done using Artificial Neural Network. The CS algorithm is found to be faster and more efficient as compared to ABC and PSO for the problem under consideration.