A Hybrid Search Algorithm of Ant Colony Optimization and Genetic Algorithm Applied to Weapon-Target Assignment Problems

An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

“…Lee et al [6] 2002 IS + ACO Static single objective Lee et al [7] 2002 GA Static single objective Lee et al [8] 2003 GA + ACO Static single objective Galati and Simaan [5] 2007 Tabu Dynamic single objective Lee [9] 2010 VLSN Static single objective Xin et al [10] 2010 VP + tabu Dynamic single objective Li and Dong [11] 2010 DPSO + SA Dynamic single objective Chen et al [12] 2010 SA Static single objective Xin et al [13] 2011 Rule-based heuristic Dynamic multiobjective Fei et al [14] 2012 Auction algorithm Static single objective Bogdanowicz et al [15] 2013 GA Static single objective Liu et al [16] 2013 MOPSO Static multiobjective Zhang et al [17] 2014 MOEA/D Static multiobjective Ahner and Parson [18] 2015 Dynamic programming Dynamic multiobjective Li et al [19] 2015 NSGA-II, MOEA/D Static multiobjective Dirik et al [20] 2015 MILP Dynamic multiobjective Hongtao and Fengju [21] 2016 CSA Static single objective Li et al [22] 2016 MDE Dynamic multiobjective Li et al [23] 2017 MPACO Static multiobjective 2 International Journal of Aerospace Engineering population members and also allow the NSGA-III to perform well on MOP with differently scaled objective values. This is an advantage of the NSGA-III and another reason why we choose the NSGA-III algorithm to solve the SMWTA problem.…”

“…However, we do not know if the hth reference point is eventually useful in advance. Under this circumstance, we simply add a set of reference points by adopting the formulas (7) and (8). The scale of new reference points equals the number of α = 0 reference points.…”

A hybrid search algorithm with heuristics for resource allocation problem

“…Two approaches are proposed to enhance the search ability for resource allocation problem in the proposed algorithm. The first approach is to use elite preserving crossover (GEX) to keep those genes supposed to be good at each generation [25]. The concept of GEX is to construct offspring with possibly good genes from parents.…”

A branch‐and‐price algorithm for a targeting problem

“…See, for example, Kwon et al [6] for a literature review of weapon-target assignment (WTA) problems before 2000. Recently, traditional WTA problems of maximizing damage done to targets were dealt with using genetic algorithms (GA) embedded with a local search approach like ant colony optimization (ACO) [4,7] or greedy eugenics [8]. Lu et al [9] proposed the GA with an improvement in terms of uniform creation of initial population, selection based on fitness scaling and selfadaptive parameters for genetic operators, etc.…”