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The energy demand estimation commands great importance for both developing and developed countries in terms of the economy and country resources. In this study, the differential evolution algorithm (DE) was used to forecast the long-term energy demand in Turkey. In addition to being employed for solving regular optimization problems, DE is also a global, meta-heuristic algorithm that enables fast, reliable and operative stochastic searches based on population. Considering the correlation between the increase in certain economic indicators in Turkey and the increase of energy consumption, two equations were used-one applying the linear form and the other the quadratic form. Turkey's long-term energy demand from 2012 to 2031 was estimated through the DE method in three different scenarios and in terms of the gross domestic product, import, export and population. To prove the success of the DE method in addressing the energy demand problem, the DE method was compared to other methods found in the literature. Results showed that the proposed DE method was more successful than the other methods. Furthermore, the future projections of energy demand obtained using the proposed method were compared to the indicators of energy demand estimated and observed by the Ministry of Energy and Natural Resources.

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Many real-world problems can be formulated as an optimization problem and they have some constraints generally. To overcome these constraints, bio-inspired algorithms are adapted to constrained optimization using constraint handling methods and some modifications. In this study, a new approach is developed to solve constrained optimization problems with elephant herding optimization algorithm which is a newly-emerging optimization technique. Besides the basic EHO, two EHO variants (EHO-NoB and GL-EHO) are adapted to constrained optimization with this approach. The well-known thirteen constrained benchmark functions are used to analysis the performances of algorithms. Experimental results show that the GL-EHO has a better performance than the basic EHO and other algorithms. In addition, the results of GL-EHO are comparable level with the result of another EHO variant in the literature.