Hybridization plays a prominent role in bolstering the performance of optimization algorithms (OAs), yet designing efficient hybrid OAs tailored to intricate optimization problems persists as a formidable task. This article introduces a novel top-down methodology for the automated design of hybrid OAs, treating algorithm design as a meta-optimization problem. A general design template for collaboration-based hybrid OAs is developed, integrating a multitude of hybridization strategies for the first time. Besides, a mathematical model is built to formulate the meta-optimization problem of algorithm design. To address the meta-optimization challenge, an improved multifactorial evolutionary algorithm is proposed to automatically design efficient hybrid metaheuristics in a multitasking environment for the given instances with diverse features. To verify the effectiveness of the proposed design methodology, it is applied to the CEC2017 benchmark functions and the binary knapsack problem. Numerical results have demonstrated the feasibility and effectiveness of the proposed methodology for both continuous and combinatorial optimization benchmarks.

This article focuses on the coalition formation (CF) problem in urgent missions, e.g., disaster rescue, where coalition members should reach mission locations quickly. A mathematical model is first constructed to minimize the latest arrival time of coalition members, considering the capability requirements of missions, nonredundant agents in coalitions, etc. Then, incorporating the benefits in both the diversity of random search and the effectiveness of utilizing problem knowledge, a local-search-based heuristic is put forward to solve the CF problem. An initial solution is incrementally constructed by prioritizing agents with shorter movement times for missions with higher-remaining capability requirements. Additionally, two types of neighborhood search operators, namely, the tabu-based one-to-one swap and the destroy and repair operators, are proposed to search the solution space from two perspectives, i.e., “adjustment” and “reconstruction.” To solve the problem effectively and efficiently, the former excludes certain agent-exchange combinations that do not improve the current solution, while the latter consists of multiple heuristic rules extracted from the correlation among different model elements. Experimental results have demonstrated that the proposed method surpasses several advanced methods across various scenarios regarding multiple factors, such as the number of agents, the number of missions, and the demand-supply ratio on capabilities.

This article investigates the joint allocation problem of stochastic resources (JASRs), which is widely found in complex systems. A general mathematical model for joint allocation of multiple heterogeneous stochastic resources is built, capturing the interdependencies between resources, quantity constraints, capability constraints, and strategy constraints of resources. An adaptive memetic algorithm (MA) is proposed for JASR, and the multipermutation encoding method is developed to denote assignment schemes of different resources. Multiple permutation-based operators are employed in the mutation and local search process under the genetic evolution framework and learning framework, respectively. Besides, a hybrid initialization method and an adaptive replacement strategy are put forward. Moreover, a restart strategy is developed to rebuild the population to maintain the genetic diversity. Twenty-five random test instances are produced to validate the effectiveness of the proposed MA. The results of computational experiments and the Wilcoxon rank-sum test demonstrate that these JASR instances can be well handled by the proposed adaptive MA, and the proposed MA is able to provide remarkably better decision schemes for the majority of the test instances than the prevailing solution methods.

In this paper, a mathematical model for the joint allocation of two heterogeneous stochastic resources (namely, sensors and actuators) is presented, addressing the interdependencies between sensors and actuators, the resource constraints, the capability constraints as well as the strategy constraints. A heuristic initialized memetic algorithm (MA) is proposed to solve the joint allocation problem about stochastic resources (JASR). The integer-based dual-permutation encoding method is adopted and several permutation-based operators are involved in the process of crossover, mutation and local search. Besides, a hybrid initialization method is employed to maintain a balance between exploration and exploitation. For the performance evaluation, we build a general Monte Carlo simulation based JASR framework. Furthermore, we employ an extension of the state-of-the-art algorithm Swt_opt, MRBCH and BMA as competitors. Computational results show that the proposed MA performs very well in solving JASR instances of different scales, and it can generate better assignment schemes in most cases than its competitors in limited time.

In network-centric warfare, the interconnections among various combat resources enable an advanced operational pattern of cooperative engagement. The operational effectiveness and outcome strongly depends on the reasonable utilization of available sensors and weapons. In this paper, a mathematical model for the coallocation of sensors and weapons is built, taking into account the interdependencies between weapons and sensors, the resource constraints, the capability constraints, as well as the strategy constraints. A marginal-return-based constructive heuristic (MRBCH) is proposed to solve the formulated sensor-weapon-target assignment (S-WTA) problem. MRBCH exploits the marginal return of each sensor-weapon-target triplet and dynamically updates the threat value of all targets. It relies only on simple lookup operations to choose each assignment triplet, thus resulting in very low computational complexity. For performance evaluation, we build a general Monte Carlo simulation-based S-WTA framework. Furthermore, we employ a random sampling method and an extension of the state-of-the-art algorithm Swt_opt as competitors. The computational results show that MRBCH consistently performs very well in solving S-WTA instances of different scales, and it can generate assignment schemes much more efficiently than its competitors.