A Multi-Objective Evolutionary Approach for the Antenna Positioning Problem

On the efficiency of the binary flower pollination algorithm: Application on the antenna positioning problem

“…Taking now the parameter ˛, some researchers such as Segura [19] stated that a decision maker must select its value. The latter is tuned considering the importance given to the coverage in relation with the number of deployed BTSs.…”

“…The authors of [88,90] used several evolutionary and nonevolutionary algorithms for benchmarking several solvers of the APP. Several multiobjective algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the adaptive and non-adaptive Indicator-Based Evolutionary Algorithm (IBEA) were also used in [19] to tackle a multiobjective formulation of the APP. Then, a parallel hyper-heuristic was devised in [20] for the antenna positioning problem.…”

A quantum-inspired genetic algorithm for solving the antenna positioning problem

“…As far as the parameter σ is concerned, some researchers such as Segura [17], stated that a decision maker must select its value. It is tuned with respect to the importance given to coverage and the relation with the number of deployed BTSs.…”

“…The authors of [76] and [78] used several evolutionary and non-evolutionary algorithms for benchmarking several solvers of the APP. Several multiobjective algorithms such as the non-dominated sorting genetic algorithm II, the strength pareto evolutionary algorithm 2 and the adaptive and non-adaptive indicator-based evolutionary algorithm were also used in [17] to tackle a multiobjective formulation of the APP. Then, a parallel hyper-heuristic was devised in [18] for the APP.…”

Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach

“…Since their inception, a broad array of algorithms and applications has been developed and refined to tackle the challenge of antenna positioning. Notable advancements include the development of the greedy algorithm [12] and the implementation of sophisticated distributed genetic algorithms [13], evolving towards more complex multiobjective approaches [14]. Furthermore, innovative variants of genetic algorithms inspired by quantum mechanics (QIGA) have been explored, proving their validity through real case studies and detailed statistical analysis [2].…”