Opposition-based krill herd algorithm applied to economic load dispatch problem

Abstract

Economic load dispatch (ELD) is the process of allocating the committed units such that the constraints imposed are satisfied and the production cost is minimized. This paper presents a novel and heuristic algorithm for solving complex ELD problem, by employing a comparatively new method named krill herd algorithm (OKHA). KHA is nature-inspired metaheuristics which mimics the herding behaviour of ocean krill individuals. In this article, KHA is combined with opposition based learning (OBL) to improve the convergence speed and accuracy of the basic KHA algorithm. The proposed approach is found to provide optimal results while working with several operational constraints in ELD and valve point loading. The effectiveness of the proposed method is examined and validated by carrying out numerical tests on five different standard systems. Comparing the numerical results with other well established methods affirms the proficiency and robustness of proposed algorithm over other existing methods. Ó 2016 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

“…The observation of BCI can lead 14 Complexity to an evaluation that the proposed method is slightly better than most methods (the improvement is from 0.0002% to +0.1030%). KHA4 [23], OBKHA [24], and EMA [30] have the same cost as the proposed method, but α-MFA [53] has slightly lower cost than IFA. However, the recalculated cost of α-MFA [53] is $15445.46, which is higher than the cost of the proposed method.…”

“…The different versions of krill herd algorithm (KHA) [23] have been used for solving different cases of the considered problem such as KHA without using genetic operation (KHA1), KHA using crossover operation (KHA2), KHA using mutation operation (KHA3), and KHA using crossover operation and mutation operation (KHA4). Several disadvantages of these KHA methods have been pointed out in [24] such as low convergence speed and low success rate. Thus, opposition-based krill herd algorithm (OBKHA) [24] has been developed by applying learning technique based on opposition and it has been compared to KHA methods.…”

“…Several disadvantages of these KHA methods have been pointed out in [24] such as low convergence speed and low success rate. Thus, opposition-based krill herd algorithm (OBKHA) [24] has been developed by applying learning technique based on opposition and it has been compared to KHA methods. As derived from the comparisons, OBKHA could be considered better than KHA methods about optimal solutions reflected via less minimum cost and stable search ability reflected via less average cost, less maximum cost, and less standard deviation; however, the convergence speed and the success rate have not been investigated since there were no comparisons of used population and used iterations.…”

An experimental study regarding Economic Load Dispatch using Search Group Optimization

“…The convergence characteristics of SGO is displayed in Figure 4. [45] and AGWO [38] optimization techniques. It can be seen from the graph and the table that the minimum cost is first reached by using the SGO algorithm and the rest of the optimization techniques take minimum time as compared to other.…”

“…Oppositional real coded chemical reaction optimization (ORCCRO) [33] has a special ability to solve the non-linear and the non-quadratic equations with smoother transition. Some new algorithms like Backtracking Search Optimization [34], Exchange market algorithm [35], enhanced particle swarm optimization [36], evolutionary particle swarm optimization [37] and ameliorated grey wolf optimization [38] in which the particle swarm optimization and the grey wolf technique have been modified to solve the ELD problem are also introduced recently. In the ameliorated grey wolf optimization, the conventional grey wolf algorithm has been modified by changing the base equation and focusing more on the exploration and exploitation phases of the algorithm.…”