Discrete swallow swarm optimization algorithm for travelling salesman problem

Cat swarm optimization for solving the open shop scheduling problem

“…Every cat is characterized by its own position, its velocity, and the flag to identify whether the cat is in the seeking mode or the tracing mode. The CSO algorithm proposed by Chu and Tsai (2006) was improved by some researchers to ameliorate its efficiency, as using average-inertia weight suggested by Orouskhani et al (2011), introducing an adaptive parameter control by Wang et al (2015), parallel cat swarm optimization by PW Tsai et al (2008), solving combinatorial optimization problem by Bouzidi and Riffi (2013), solving the clustering problem improved by Razzaq et al (2016), enhanced parallel cat swarm optimization based on the Taguchi method by Tsai et al (2012). It was also extended to solve multi-objective problems in 2012 by Pradhan and Panda (2012).…”

“…About the inertia weighted parameter w, this paper had used a fix parameter values such that it was consider in the proposal of CSO to solve combinatorial problem (Bouzidi and Riffi 2014b), that values were analyzed and discussed by the application to solve the TSP problem (Bouzidi and Riffi 2013). After that it was applied to solve other combinatorial problems by using this parameters values, such as the QAP (Riffi and Bouzidi 2014), JSSP (Bouzidi and Riffi 2014a) and FSSP (Bouzidi and Riffi 2015).…”

Improved meta-heuristic techniques for simultaneous capacitor and DG allocation in radial distribution networks

“…In (22), the cat is directed by the history of its own movements and it gets momentum by the inertia constant w. Thus, the cat gathers some velocity among its all dimensions before entering into the second phase of the tracing mode. This mode is identical to the standard CSO except that the control equation also includes the inertia constant as given below: v qþ1;db ¼ w  v q;db þ c  rðÞ Â ðx best;db À x q;db Þ ð 24Þ…”

“…It has been successfully applied to solve diverse engineering optimization problems such as IIR system identification [18], clustering [19], Linear antenna array synthesis [20], linear phase FIR filter design [21], Traveling salesman problem [22], deployment of Wireless sensor networks (WSNs) [23], etc. However, the CSO has not been attempted for the optimal allocation of SCs and DGs in distribution systems.…”

Comparative analysis of Three Metaheuristics for Solving the Travelling Salesman Problem

“…There is also the exact methods, but it can only solve the TSP instances problem with minimal size, but when the number of city is high, its impossible to find the global solution. But after using the metaheuristic method, it's possible to find the best optimal solution in a reasonable execution time, as the simulated annealing [3], Tabu search [4], Harmony search [5], cuckoo search algorithm [6], Genetic algorithm [7], Ant colony optimization [8], Particle swarm optimization [9], Bee colony optimization [10], cat swarm optimization algorithm [11], bat swarm optimization [12]. To improve the efficiency of some methods, the researchers had proposed some hybrid methods that had proven their efficiency to solve the TSP, such as, the hybrid genetic algorithm [13], hybrid antcolony optimization [14], hybrid particle swarm optimization [15], hybrid bee colony optimization [16].…”