Adaptive particle swarm optimization approach for static and dynamic economic load dispatch

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

“…The dynamic economic dispatch (DED) problem has been extensively studied by SI algorithms such as BFO [242], PSO [243,244,245,246], FA [247], AFSO [248] and ABC algorithms [249,250,251,252,253]. Moreover, a hybrid SFLA algorithm [254] outperformed PSO-and ABC-based algorithms.…”

Water Evaporation Optimization Algorithm for Solving Dynamic Economic Dispatch

“…The proposed WEO algorithm achieve the best cost of 42993.6318($), worst cost of 43089.63($), and mean cost of 43009.74($). From the comparison it is clear that the proposed algorithm achieve the best results in comparison with SA [8], APSO [28], AIS [9], TLA [19] and MTLA [19]. The cost convergence characteristic curve is depicting in Fig 2. The convergence curve demonstrate that the cost is converged from larger value to smaller value ensure that the WEO algorithm is capable of producing better results than existing algorithms.…”

“…To overcome this deficiency, turn to various heuristic techniques such as Genetic Algorithm (GA) [7], Simulated Annealing (SA) [8], Artificial Immune System (AIS) [9], Evolutionary Programming (EP) [10], Differential Evolution (DE) [11], Harmony Search (HS) [12], Artificial Bee Colony (ABC) [13], Imperialist Competitive Algorithm (ICA) [14], Seeker Optimization Algorithm (SOA) [15], Teaching Learning Algorithm (TLA) [16], Improved Particle Swarm Optimization (IPSO) [17], Chaotic Differential Evolution (IDE) [18], Modified Teaching Learning Algorithm (MTLA) [19], Self-Adaptive Modified Firefly Algorithm (SAMFO) [20], Improve Pattern Search (IPS) [21], Enhanced Cross Entropy (ECE) [25], Adaptive Particle Swarm Optimization (APSO) [28], Enhanced Bee Swarm Optimization (EBSO) [35], Deterministic Guided Particle Swarm Optimization (DGPSO) [37]. The main drawback of these heuristic techniques gives the results but struck the local minima and lack of guarantee of convergence infinite time for large scale DED problems.…”

Application of Krill Herd and Water Cycle Algorithms on Dynamic Economic Load Dispatch Problem

“…However, these classical methods have convergence difficulties, specially on more complicated problems which cause the algorithm get stuck at local minima. Recently, stochastic methods such as Simulated annealing (SA) [2], Genetic algorithm (GA) [3], Particle swarm optimization (PSO) [3], Adaptive PSO (APSO) [4], Artificial bees colony (ABC) [3], MLS [5], IPS [6], BCO-SQP [7], ECE [8], Artificial immune system (AIS) [9], ,AHDE [1], Differential evolution (DE) [10], CDE3 [11], have been proposed to solve different problems of DED. Although these heuristic methods usually provide reasonable solutions which can be achieved fast, but do not always guarantee discovering the globally optimal solution, giving results near global optimum, with long execution time when meeting more complicated problems with more local optima.…”