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This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index ( Q A B / F ), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure ( N A B / F ). The average Q A B / F = 0.765508 , S C D = 1.63185 , S S I M = 0.726317 , and N A B / F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon's and Friedman's tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test. [ABSTRACT FROM AUTHOR]

Optimization of scheduling problems that involve single or multiple machines with a multimodal objective function and linear or non-linear constraints are generally NP-Hard. The Artificial Bee Colony (ABC) algorithm is a metaheuristic proposed about eighteen years ago and has gained popularity in the swarm intelligence-based evolutionary computation approaches. Various scheduling requirements in diverse domains viz. Electric Vehicle charging, Operating Room, Nurse, Automatic Guided Vehicle (AGV), production, manufacturing, hydrothermal, Printed Circuit Board (PCB), and call centre scheduling are studied. The NP-Hard nature of these scheduling problems render exact methods incompetent for large problem instances thus requiring meta-heuristics such as ABC to produce feasible solutions in a reasonable computation time. The adaptation of ABC in these domains and the outcomes and performance of ABC are highlighted. Based on the case studies, it is found that the requirements of scheduling problems that would benefit from ABC are those that have a planning horizon requiring a near-optimal solution in a reasonably fast time when run on minimal computing capacity. Future works that would merit the application and enhancement of existing ABC techniques are proposed. The case studies presented in this paper motivate R&D Engineers and young researchers to apply ABC techniques of optimization and scheduling in different research topics for achieving energy and cost efficiency. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of Chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from the perspective of multi-strategy mixing, MSChimp was proposed, and the algorithm was applied to global optimization and minimum spanning tree problems. The main research work of this paper is as follows: (1) In the initialization stage of ChOA, an opposition-based learning strategy was introduced to improve the population diversity; Sine Cosine Algorithm (SCA) was introduced in the exploitation process to improve the convergence speed and accuracy of the algorithm in the later stage, so as to balance the exploration and exploitation capabilities of the algorithm. (2) The improved algorithm was compared with different types of meta-heuristic algorithms in 20 benchmark functions and CEC 2019 test sets, and was used to solve the minimum spanning tree. The experimental results show that the improved ChOA has significantly improved the ability to find the optimal value, which verifies the effectiveness and feasibility of MSChimp. Compared with other algorithms, the algorithm proposed in this paper has strong competitiveness. [ABSTRACT FROM AUTHOR]

Introduction: The development of different optimization algorithms following the No Free-Lunch Theory has led the researchers to explore and develop new optimization algorithms which may be used to perform complicated mathematical solutions. Objectives: This paper proposes a novel Pigeon Nesting Algorithm (PNA), a single solution-based SI algorithm, which is capable of escaping the local optima due to the combination of nesting and homing behaviour of pigeon and provide accurate optimization results. Method: In order to check the validity of proposed PNA algorithm, it has been applied to the 29 benchmark functions and compared with the existing single solution and population-based meta-heuristic algorithms. In order to check the application PNA for SHM of a practical structure, its performance for damage detection of ASCE Benchmark structure using stiffness-based objective function has been compared with other well-known optimization algorithms. Results: The PNA gives good competition to the other algorithms when tested for the 29 benchmarks functions. PNA proves to be a robust algorithm for health monitoring of ASCE benchmark structure as it shows an approximate error of 1%. It has also been found that the algorithm can be used for detecting damage even in the presence of noise of 5%. Conclusion: The algorithm also outperforms other single solution-based algorithms when used for optimization of design problems. [ABSTRACT FROM AUTHOR]