Application of metaheuristic algorithm to identify priority parameters for the selection of feasible location having optimum wave energy potential

The advances in technology and demand for luxury have induced increase in energy demand. However, the conventional energy resources due to its cost and impact on environment are becoming infeasible solution to satisfy the demand. As a result, alternative sources of energy such as solar, hydro, and sustainable energy resources are used to substitute the fossil fuels and satisfy the marginal demand for energy. Wave energy has a potential to satisfy the present energy demand. However, the expensive conversion procedures are one of the major deterrents which presented wider utilization of the resource. One of the reasons of costly conversion procedure is subjective- and experience-based selection of location and infeasible selection of parameters to identify a location from where maximum resources can be utilized under maximum expenditure. That is why, selection features by objective and unbiased method can reduce the cost of conversion and maximize the resource utilization. In this aspect, a new concept of “optimization techniques as multicriteria decision-making” was used to identify the major features in an objective and unbiased manner and compared with the results from analytical hierarchy process multicriteria decision-making method technique. While comparing the significance of the parameters wind speed, wave height, wind duration, water depth, fetch, and marine engineer, academicians and stakeholder criteria were used. The most significant parameters were evaluated with respect to the optimal as well as uncertain scenarios. The result shows that wind speed is the most significant parameter under normal and positive extreme scenario, respectively. The wind speed, water depth, and wave height are the most significant parameters with respect to optimization techniques such as differential evolution and genetic algorithms criteria for this normal and for the uncertain scenario. The index also provided a heuristic and cognitive optimal value to way from a suitability of utilization efficiency of wave energy power. Both models were able to fit the data well, with R2 values of 0.99127 and 0.97964 for the linear regression model and the polynomial neural network model, respectively. It was also found that the test data set had a mean absolute error of 0.07742 for the polynomial neural network model, while it was 0.06987 for the regression model.