Real-world problems such as scientific, engineering, mechanical, etc., are multi-objective optimization problems. In order to achieve an optimum solution to such problems, multi-objective optimization algorithms are used. A solution to a multi-objective problem is to explore a set of candidate solutions, each of which satisfies the required objective without any other solution dominating it. In this paper, a population-based metaheuristic algorithm called an artificial electric field algorithm (AEFA) is proposed to deal with multi-objective optimization problems. The proposed algorithm utilizes the concepts of strength Pareto for fitness assignment and the fine-grained elitism selection mechanism to maintain population diversity. Furthermore, the proposed algorithm utilizes the shift-based density estimation approach integrated with strength Pareto for density estimation, and it implements bounded exponential crossover (BEX) and polynomial mutation operator (PMO) to avoid solutions trapping in local optima and enhance convergence. The proposed algorithm is validated using several standard benchmark functions. The proposed algorithm’s performance is compared with existing multi-objective algorithms. The experimental results obtained in this study reveal that the proposed algorithm is highly competitive and maintains the desired balance between exploration and exploitation to speed up convergence towards the Pareto optimal front.

This paper aims to examine patient prioritization challenges faced by surgeons attending to patients awaiting surgery and proposes a decision-making framework named PSWL-CCI to prioritize patients in the surgical waiting list. The proposed framework deals with two critical issues: One, to prioritize patients from the surgical waiting list. Two, to refine and optimize cosine consistency index (CCI) of inconsistent pairwise comparison matrix (PCM) and obtain consistent priorities. The judgment of surgeons on identified parameters in the term of rating helps in determining priorities from the surgical waiting list. The cosine maximization method (CM), along with the analytic hierarchy process (AHP), is used to evaluate the resulting priority. To improve inconsistent pairwise comparison matrix (PCM), a novel hybrid algorithm HMWCA (Hybrid modified water cycle algorithm), is proposed and incorporated in PSWL-CCI. The proposed algorithm exploits the feature of three traditional algorithms, namely the evaporation-based water cycle algorithm (ER-WCA), genetic algorithm, and 2-opt heuristic. In this paper, the concept of salt concentration and absorption introduced into the evaporation rate (ER) that extends ER-WCA to a modified water cycle algorithm (MWCA). MWCA iteratively modifies the entries in PCM until PCM is optimized. The genetic algorithm helps MWCA to determine the evaporation rate and enhance the rate of convergence. The 2-OPT algorithm improvises the optimal solution. The proposed algorithm is tested with different datasets, and the improved CCI values are validated through paired sample t-test. Finally, the proposed PSWL-CCI framework is validated through a case study of a real patient dataset from an orthopedic surgery department of a multispecialty hospital in India. The experimental results obtained in this study reveal that the proposed methodology and algorithms significantly improve the CCI values, thus generating optimum priorities for the patients of the surgical waiting list.

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Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded space and lose generalizability to unseen classes. This paper proposes a noise-assisted representation learning method for fine-grained image retrieval to mitigate these issues. In the proposed work, class manifold learning is performed in which positive pairs are created with noise insertion operation instead of tightening class clusters. And other instances are treated as negatives within the same cluster. Then a loss function is defined to penalize when the distance between instances of the same class becomes too small relative to the noise pair in that class in embedded space. The proposed approach is validated on CARS-196 and CUB-200 datasets and achieved better retrieval results (85.38% recall@1 for CARS-196% and 70.13% recall@1 for CUB-200) compared to other existing methods.