The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

All Network intrusion detection is designed for detecting, preventing, and repelling network security breaches and it has become an urgent issue. Maintaining a safe and secure network requires an efficient and flexible solution called an intrusion detection system. This paper reports an advanced intrusion detection method created with a deep learning approach. Evolutionary operators can reduce the probability of stagnation in local solutions due to high local optima avoidance and have thus superseded conventional training algorithms, such as back propagation (BP). Combining a deep neural network (DNN) and an evolutionary algorithm (EA) may solve problems or outperform DNN in solving existing problems. We develop a hybrid training method that combines simulated annealing (SA) and BP to improve the performance of DNN (SABP-DNN). The NSL-KDD dataset is used to verify the accuracy and efficiency of the proposed method. The proposed method is also compared with the original DNN based on PB (PB-DNN) and DNN based on SA (SA-DNN). We confirm that the proposed method presents a strong potential to become an alternative solution to IDS through experiments and comparisons with existing methods.