The stochastic resource allocation (SRA) problem, commonly seen in the decision-making of complex systems, is a typical combinatorial optimization challenge that seeks optimality, quickness, as well as generality. Among various SRA algorithms, constructive heuristics (CH) such as greedy algorithms construct feasible solutions by prioritizing optional resource-task allocation pairs based on a predefined evaluation criterion. They are very suitable for real-time decision-making due to their simplicity and low computational complexity. However, relying on a single fixed criterion can impair optimality and generality. To achieve a generalized expression for the criterion, this paper establishes a fuzzy evaluation system that determines the component priorities by leveraging SRA problem features, enabling diversified and flexible solution construction. Furthermore, for the sake of generality, this paper proposes a fuzzy-evaluation-based constructive heuristics generation framework (FCHG), which generates an ensemble of complementary CHs through automatic training. FCHG adopts an adversarial coevolution mechanism, using the SRA instance generator and evolutionary algorithms to realize competition-based coevolution between the SRA instances and CHs. For SRA problem-solving, the CH ensemble obtained by FCHG can construct multiple SRA solutions efficiently, and the best one will serve as the final solution. Comparative experiments against state-of-the-art algorithms, covering instances with varying scales and structural characteristics, demonstrate the comprehensive superiority of the CH ensemble in solving the SRA problem in terms of optimality, quickness, generality, and numerical stability.

The stochastic resource allocation (SRA) problem is widely encountered in complex systems, where the resource may probabilistically fail to complete its assigned task. In practical scenarios, the assignment of resources to tasks should be handled within specified time windows, and the success probability of each assignment changes over time. Such a problem can be represented as the SRA problem with time window (SRA-TW). Both the discrete assignment relationship and the corresponding continuous-valued assignment time are indispensable in the decision scheme of SRA-TW. This mixed-variable nature poses a great challenge for optimization. Based on these requirements, SRA-TW is formulated as a mixed-variable optimization problem (MVOP) with temporal constraints. To solve this problem, an adaptive hybrid algorithm with clustering-based diversity preservation (AHACDP) is proposed. Firstly, a variable-length hybrid encoding method with constructive decoding is proposed for incremental constraint handling. Secondly, a hybrid search mechanism incorporating a matching-similarity-guided adaptive selection method is proposed to balance the search in discrete and continuous subspaces. Then, a clustering-based diversity preservation strategy is developed, facilitating a good distribution of the population. Finally, an SRA-TW instance generator considering various problem features is designed, so as to comprehensively validate the algorithm’s performance. The statistical results over numerous instances demonstrate the superiority ofAHACDPover prevailing algorithms in addressing SRA-TW.

Many scientific applications can be well modelled as large-scale workflows. Cloud computing has become a suitable platform for hosting and executing them. Workflow scheduling has gained much attention in recent years. However, since cloud service providers must offer services for multiple users with various QoS demands, scheduling multiple applications with different QoS requirements is highly challenging. This work proposes a Multi-swarm Co-evolution-based Hybrid Intelligent Optimization (MCHO) algorithm for multiple-workflow scheduling to minimize total makespan and cost while meeting the deadline constraint of each workflow. First, we design a multi-swarm co-evolutionary mechanism where three swarms are adopted to sufficiently search for various elite solutions. Second, to improve global search and convergence performance, we embed local and global guiding information into the updating process of a Particle Swarm Optimizer, and develop a swarm cooperation technique. Third, we propose a Genetic Algorithm-based elite enhancement strategy to exploit more non-dominated individuals, and apply the Metropolis Acceptance rule of Simulated Annealing to update the local guiding solution for each swarm so as to prevent it from being stuck into a local optimum at an early stage. Extensive experimental results demonstrate that MCHO outperforms the state-of-art scheduling algorithms with better distributed non-dominated solutions.

Since a single private cloud cannot satisfy the increasing computational requirements for executing multiple workflows simultaneously, hybrid clouds are often adopted to perform such execution. Multi-workflow scheduling is challenging as users may request various applications with different QoS requirements. This work proposes a Chaotic-nondominated-sorting Owl Search Algorithm (COSA) by combining an Owl Search Algorithm (OSA) with a Nondominated Sorting Genetic Algorithm II (NSGA-II) to schedule resource-constrained multiple workflows in hybrid clouds with makespan, cost and energy consumption minimized under the given deadline and budget constraints. First, a hierarchical evolving mechanism is designed to update the better half and worse half of population by NSGA-II and OSA, respectively to guarantee a good trade-off between exploration and exploitation. Second, a chaotic sequence is introduced to adaptively adjust OSA's step size during population evolution for better exploration. Third, we adopt a chaotic operator for searching around the resulting Non-Dominated Solutions (NDS) to improve COSA's local search ability. Experiments are conducted to compare COSA with four peers and the results show its superiority in the number of obtained NDS, diversity preservation and convergence towards the near optimal Pareto set. In particular, it can find at least 19% more NDS than its peers.