Lion optimization algorithm for team orienteering problem with time window

Abstract

<span>Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate a trespass strong nomadic lion to prevail a pride by fighting its males. As a result of this improving process, an enhanced LA (ILA) emerged. The obtained solutions have been compared with the best known and standard results obtained in the former studies. The conducted experimental test verifies the effectiveness of the ILA in solving the TOPTW as it obtained a very competitive results compared to the LA and the state-of-the-art methods across all tested instances.</span>

Determining Reliable Solutions for the Team Orienteering Problem with Probabilistic Delays

“…The main problem with exact methods is that the size of the TOP instances it can solve is usually reduced to a few hundred nodes, so many proposed metaheuristics can cope with large instances and reduce computational times to find near optimal solutions for the TOP problem. It includes Tabu Search [12,20], Variable Neighborhood Search algorithms [21], Greedy randomized adaptive search procedure with path re-linking [22], Ant Colony [23], Particle Swarm Optimization [13], Lion Optimization Algorithm [24], Simulated Annealing [25], Memetic Algorithm [26], Pareto Mimic Algorithm [27], Guided Local Search [21], Hybrid Harmony Search [28], or Genetic Algorithms [29,30]. These approaches improve the results obtained by the former heuristics, but the computational time to obtain a near-optimal solution also increases.…”