Disasters are large-scale disruptions for the society that cause damage to human lives, infrastructure, and the ecosystem. Multiple emergencies emerge, which need to be handled and resolved efficiently to minimize the impact of the disaster. Therefore, the need for an effective and efficient emergency response system that optimally allocates resources and emergency services is essential. This paper proposes a methodology to model this situation as an optimization problem that can be solved using meta-heuristic algorithms. In the proposed model, four meta-heuristics, namely Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, and iLSHADE-RSP algorithm, have been used to allocate resources and services. A benchmark dataset consisting of 16 situations is prepared to analyze the proposed model. The conducted empirical analysis demonstrates the applicability of the meta-heuristic algorithms in locating near-optimum solutions for the considered situations. The convergence analysis and statistical tests have been performed to test the validity and significance of the conducted experiments.

No abstract

No abstract

A novel meta-heuristic algorithm named as the Cell Division Optimizer (CDO) is proposed. The proposed algorithm is inspired by the reproduction methods at the cellular level, which is formulated by the well-known cell division process known as mitosis and meiosis. In the proposed model Meiosis and Mitosis govern the exploration and exploitation aspects of the optimization algorithm, respectively. In the proposed method, the solutions are updated in two phases to achieve the global optimum solution. The proposed algorithm can be easily adopted to solve the combinatorial optimization method. To evaluate the proposed method, 50 well-known benchmark test functions and also 2 classical engineering optimization problems including 1 mechanical engineering problem and 1 electrical engineering problem are employed. The results of the proposed method are compared with the latest versions of state-of-the-art algorithms like Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, FruitFly Optimization, Whale Optimizer, Water-Wave Optimizer and recently proposed variants of top-performing algorithms like SHADE (success history-based adaptive differential evolution) and CMAES (Covariance matrix adaptation evolution strategy). Moreover, the convergence speed of the proposed algorithm is better than the considered competitive methods in most cases.

Disasters are large-scale disruptions for the society that cause damage to human lives, infrastructure, and the ecosystem. Multiple emergencies emerge, which need to be handled and resolved efficiently to minimize the impact of the disaster. Therefore, the need for an effective and efficient emergency response system that optimally allocates resources and emergency services is essential. This paper proposes a methodology to model this situation as an optimization problem that can be solved using meta-heuristic algorithms. In the proposed model, four meta-heuristics, namely Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, and iLSHADE-RSP algorithm, have been used to allocate resources and services. A benchmark dataset consisting of 16 situations is prepared to analyze the proposed model. The conducted empirical analysis demonstrates the applicability of the meta-heuristic algorithms in locating near-optimum solutions for the considered situations. The convergence analysis and statistical tests have been performed to test the validity and significance of the conducted experiments.