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<p>The Reliability-Redundancy Allocation Problem (RRAP) is a nonlinear, mixed-integer, and NP-hard optimization problem that plays a critical role in enhancing system performance under resource constraints. This study investigates a multi-objective formulation of RRAP in the context of smart waste-to-energy conversion systems, aiming to simultaneously maximize system reliability and minimize overall cost, subject to weight and volume limitations. To address this challenging problem, we propose a Modified Ant Lion Optimizer (MALO), a robust metaheuristic algorithm tailored for Pareto-based multi-objective optimization. MALO incorporates an adaptive mechanism for balancing exploration and exploitation, along with dynamic population partitioning to efficiently navigate the high-dimensional solution space. In contrast to traditional approaches, MALO explicitly handles the trade-offs among reliability, cost, weight, and volume using non-dominated sorting and an elitist archive strategy. The proposed algorithm is validated on a realistic waste-to-energy system model. The results demonstrate that MALO can achieve a system reliability of up to 90% while maintaining optimized cost values. This work contributes to the advancement of applied reliability engineering by bridging the gap between theoretical metaheuristics and practical system design in the domain of sustainable energy technologies.</p>

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A balanced metaheuristic approach to optimize reliability and cost of smart waste-to-energy conversion system

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Jaya Choudhary,
  • Ashok Singh Bhandari,
  • Mangey Ram

摘要

The Reliability-Redundancy Allocation Problem (RRAP) is a nonlinear, mixed-integer, and NP-hard optimization problem that plays a critical role in enhancing system performance under resource constraints. This study investigates a multi-objective formulation of RRAP in the context of smart waste-to-energy conversion systems, aiming to simultaneously maximize system reliability and minimize overall cost, subject to weight and volume limitations. To address this challenging problem, we propose a Modified Ant Lion Optimizer (MALO), a robust metaheuristic algorithm tailored for Pareto-based multi-objective optimization. MALO incorporates an adaptive mechanism for balancing exploration and exploitation, along with dynamic population partitioning to efficiently navigate the high-dimensional solution space. In contrast to traditional approaches, MALO explicitly handles the trade-offs among reliability, cost, weight, and volume using non-dominated sorting and an elitist archive strategy. The proposed algorithm is validated on a realistic waste-to-energy system model. The results demonstrate that MALO can achieve a system reliability of up to 90% while maintaining optimized cost values. This work contributes to the advancement of applied reliability engineering by bridging the gap between theoretical metaheuristics and practical system design in the domain of sustainable energy technologies.