Hybrid optimization with improved tabu search

A Review of Potential Method for Optimization of Power Plant Expansion Planning in Jawa-Madura-Bali Electricity System

“…The results of [25,[44][45][46] showed that the optimization results using TS still has a possibility to get stuck in a local optimum value. To get the best result need to take several simulations and using a different number of individual neighbors and tabu list in each simulation to obtain the most optimal value.…”

Hybrid Simulated Annealing and Nelder-Mead Algorithm for Solving Large-Scale Global Optimization Problems

“…SNMRVU algorithm can obtain better function values if we apply one termination criterion, which is the number of evaluation function values is ≤ 50, 000. 7.14e-11 9.06e-10 3.34e-09 F 3 5.57e-11 6.56e-11 2.12e-09 F 4 (0.0) (0.0) (0.0) F 5 6.01e-07 1.54e-05 4.32e-05 F 6 0 (0.0) (0.0) F 7 3.85e-07 2.11e-06 9.85e-06 F 8 4.9e-07 1.42e-14 2.81e-14 F 9 (0.0) (0.0) (0.0) F 10 4.3e-07 3.12e-04 1.12e-04…”

“…Many promising methods have been proposed to solve the mentioned problem in Equation 1, for example, genetic algorithms [14,24], particle swarm optimization [23,28], ant colony optimization [31], tabu search [9,10], differential evolution [2,6] scatter search [15,20], and variable neighborhood search [11,27]. Although the efficiency of these methods, when applied to lower and middle dimensional problems, e.g., D < 100, many of them suffer from the curse of dimensionality when applied to high dimensional problems.…”

Solving optimization problems using black hole algorithm

“…Metaheuristic approaches have been formed according to inspiration by nature, physics and human being. In recent years, many of these algorithms and their improved algorithms have been successfully applied to various problems of engineering optimization [12][13][14][15][16]. A common feature in meta-heuristic approaches is that they combine rules and randomness to imitate natural phenomena.…”

“…A common feature in meta-heuristic approaches is that they combine rules and randomness to imitate natural phenomena. These phenomena include the biological evolutionary process (e.g., the Genetic Algorithm (GA) [17][18] and the Differential Evolution (DE) [12][13]), animal behavior (e.g., Particle Swarm Optimization (PSO) [14] and Ant Colony Algorithm (ACA) [15][16]), and the physical annealing process (e.g., Simulated Annealing (SA) [1,19]). These algorithms are one of the approximate optimization approaches that have mechanism of departing from local optimum.…”