Dual nest pigeon-inspired optimization and its application in engineering constrained optimization problems

A new dual-population constrained multi-objective evolutionary optimization algorithm with repair constraint handling for structural optimization

This study introduces a novel constrained multi-objective evolutionary algorithm, termed DPCME, which employs two interacting populations that exchange information, enabling effective global exploration and reducing the risk of convergence to local optima.

Enhanced crested porcupine optimizer for global optimization and constrained engineering problems

Balancing Exploration and Exploitation Through a Sequential Hybrid of Roach Infestation and Mayfly Algorithms for Constrained Engineering Design Optimization

Maintaining an effective balance between exploration and exploitation is essential during optimization processes, from mathematical functions to more complex problems such as constrained engineering design optimization, particularly when addressing highly nonlinear issues with numerous local optima and strict feasibility requirements. This manuscript introduces a sequential hybrid metaheuristic that integrates the global search virtues of the roach infestation optimization (RIO) framework with the local refinement strengths of the mayfly algorithm (MA) to boost search efficiency in constrained optimization problems. This strategy leverages the best of both algorithms, thereby increasing population diversity, accelerating convergence, and improving solution quality while maintaining feasible solutions within the engineering constraints. Performance was evaluated using various classic engineering design benchmark problems widely used in the optimization literature. Experimental results evaluated solution quality and population diversity, comparing the proposed method with the original RIO and MA algorithms and with other known metaheuristic approaches. In the statistical validation, distribution‐free statistical tests were performed to evaluate the importance of the generated outcomes. Our observations indicate that the introduced sequential hybrid scheme attains an enhanced coordination of global and local search, enabling the system to mitigate early stagnation and improve optimization performance in restricted search spaces. This provides a highly viable and dependable alternative for engineering optimization layouts and establishes a foundation for future adaptive and fuzzy logic‐based hybrid optimization approaches.

Multi-objective chaos game optimization for constrained structural design using chaos-inspired search dynamics

This study introduces Multi-Objective Chaos Game Optimization (MOCGO), a metaheuristic algorithm developed for the purpose of handling multi-objective structural optimization problems with constraints. In doing so, MOCGO utilizes a single-objective algorithm known as Chaos Game Optimization (CGO) in conjunction with chaos exploration dynamics, a diversity-preserving archive, and a grid-selection process to create an effective set of Pareto optimal solutions. This algorithm has been tested on six different trusses (10-bar to 120-bar) based on the problem of minimizing the weight and compliance of a structure while satisfying the stress constraint criteria. As measures of fitness, MOCGO performs significantly better than other algorithms, including NSGA-II, MOGOA, MOALO, and MOAVOA. These results demonstrate MOCGO as an efficient and robust approach for constrained multi-objective structural design.

Cat Reflex Optimization (CRO): a multi-stage metaheuristic for high-dimensional and constrained engineering optimization

Camel foraging optimization algorithm: a mechanism-driven metaheuristic with fractional memory for constrained optimization

Global optimization problems often involve nonlinear, multimodal, non-separable, and constrained search landscapes that challenge population-based metaheuristics under limited function-evaluation budgets. This paper proposes the camel foraging optimization algorithm (CFEOA), a mechanism-driven metaheuristic that combines endurance-regulated state control with bounded Grünwald–Letnikov fractional-order displacement memory. In CFEOA, endurance acts as an agent-specific variable regulating the transition between exploration and local refinement, while the bounded fractional-memory term introduces directional persistence from recent accepted displacements. The camel-foraging analogy is used only as an organizing metaphor; the search process is defined through explicit mathematical operators, boundary repair, acceptance rules, and function-evaluation accounting. CFEOA is evaluated using 30 independent runs, fixed random seeds, equal function-evaluation budgets, disabled early stopping in the main benchmark comparisons, full parameter disclosure, and public code availability. The main validation uses the CEC 2022 suite with classical swarm baselines and adaptive DE-family comparators, including NL-SHADE-LBC as an additional recent SOTA reference. Across 18 function–dimension blocks, NL-SHADE-LBC achieves the most favorable aggregate rank, whereas CFEOA retains statistically significant advantages over PSO, GWO, and WOA and shows no significant difference from DE, JADE, SHADE, L-SHADE, and TLBO after Holm correction. Component-wise ablation, parameter sensitivity, convergence, diversity, CPU-time, scalability, and constrained engineering analyses indicate that CFEOA provides a transparent, reproducible, and reliability-oriented search framework with landscape-dependent strengths rather than universal SOTA dominance. The results position CFEOA as a controlled-search alternative for selected complex and constrained optimization settings where repeatability, feasibility, and auditable mechanism design are important.