A hybrid multi‐objective tour route optimization algorithm based on particle swarm optimization and artificial bee colony optimization

Abstract

SummaryComputational intelligence techniques have widespread applications in the field of engineering process optimization, which typically comprises of multiple conflicting objectives. An efficient hybrid algorithm for solving multi‐objective optimization, based on particle swarm optimization (PSO) and artificial bee colony optimization (ABCO) has been proposed in this paper. The novelty of this algorithm lies in allocating random initial solutions to the scout bees in the ABCO phase which are subsequently optimized in the PSO phase with respect to the velocity vector. The last phase involves loyalty decision‐making for the uncommitted bees based on the waggle dance phase of ABCO. This procedure continues for multiple generations yielding optimum results. The algorithm is applied to a real life problem of intercity route optimization comprising of conflicting objectives like minimization of travel cost, maximization of the number of tourist spots visited and minimization of the deviation from desired tour duration. Solutions have been obtained using both pareto optimality and the classical weighted sum technique. The proposed algorithm, when compared analytically and graphically with the existing ABCO algorithm, has displayed consistently better performance for fitness values as well as for standard benchmark functions and performance metrics for convergence and coverage.

Bees algorithm for Forest transportation planning optimization in Malaysia

“…Since they unable to consider multi-objective, including traffic time, maintenance, breakage, and emergency conditions in their optimization, hence, this has become a limitation in their study. In different traveling problems with the tourist spot-constraints, Beed et al (2020) combined BA with PSO to optimize tour routes by visitors. They used multi-objectives function to meet the minimum travel cost, distance and time in terms of deviation between tourist spots, and maximized the number of tourist spots to be visited.…”

“…Although previous research discussed above focused on road network problem in urban areas with BA, but the concepts of solutions they tried to elucidate were similar with forest transportation problem in Malaysian forest. For example, the extraction of log from stump site to landing can be represented with the concepts of serving the communities to reach their targeted location within a minimum travel time and finding the shortest routes (Long et al 2014;Iqbal et al 2015;Ng et al 2017;Alzaqebah et al 2018;Chen and Zhou 2018;George and Binu 2018;Santosh and Suresh 2019;Beed et al 2020;Karaoglan et al 2020). By shortening the traveling time, the cost for timber extraction can be reduced, thus the operational efficiency can be increased.…”

Load Distribution Optimization of Multi-Source District Heating System Based on Fuzzy Analytic Hierarchy Process

“…As for the optimization algorithm selection, particle swarm optimization (PSO) has been widely used in the multiobjective optimization (MOO) field [24] [25]. It is a global search algorithm first proposed by Eberhart and Kenedy in 1995 [26].…”

“…Inertia weight w refers to the ability of a particle to maintain the previous state of motion. At present, the setting methods of inertia weight mainly include constant setting, linear adjustment, fuzzy adaptive, and random adjustment [24] [25]. Shi et al suggested setting the inertia weight to 0.8 [47].…”

Multi‐swarm competitive swarm optimizer for large‐scale optimization by entropy‐assisted diversity measurement and management

“…As a successful SI algorithm, PSO has been popular to many different kinds of optimization problems because of its straightforward structure but satisfactory performance 10 . The applications include diseases diagnosis, 11 path planning, 12 engineering design, 13 energy delivering system, 14 network control, 15,16 and so on 17 . In PSO, each candidate solution is regarded as a particle which has two attributes, namely velocity and position, respectively.…”