This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

The vehicle particle model was built to compare and analyze the effectiveness of three different collision avoidance methods. The results show that during vehicle high-speed emergency collision avoidance, lane change collision avoidance requires a smaller longitudinal distance than braking collision avoidance and is closer to that with a combination of lane change and braking collision avoidance. Based on the above, a double-layer control strategy is proposed to avoid collision when vehicles change lanes at high speed. The quintic polynomial is chosen as the reference path after comparing and analyzing three polynomial reference trajectories. The multiobjective optimized model predictive control is used to track the lateral displacement, and the optimization objective is to minimize the lateral position deviation, yaw rate tracking deviation, and control increment. The lower longitudinal speed tracking control strategy is to control the vehicle drive system and brake system to track the expected speed. Finally, the lane changing conditions and other speed conditions of the vehicle at 120 km/h are verified. The results show that the control strategy can track the longitudinal and lateral trajectories well and achieve effective lane change and collision avoidance.

We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA).

The Vehicle Routing Problem (VRP) and its variants are found in many fields, especially logistics. In this study, we introduced an adaptive method to a complex VRP. It combines multi-objective optimization and several forms of VRPs with practical requirements for an urban shipment system. The optimizer needs to consider terrain and traffic conditions. The proposed model also considers customers\' expectations and shipper considerations as goals, and a common goal such as transportation cost. We offered compromise programming to approach the multi-objective problem by decomposing the original multi-objective problem into a minimized distance-based problem. We designed a hybrid version of the genetic algorithm with the local search algorithm to solve the proposed problem. We evaluated the effectiveness of the proposed algorithm with the Tabu Search algorithm and the original genetic algorithm on the tested dataset. The results show that our method is an effective decision-making tool for the multi-objective VRP and an effective solver for the new variation of VRP.

A multitude of membrane active peptides exists that divides into subclasses, such as cell penetrating peptides (CPPs) capable to enter eukaryotic cells or antimicrobial peptides (AMPs) able to interact with prokaryotic cell envelops. Peptide membrane interactions arise from unique sequence motifs of the peptides that account for particular physicochemical properties. Membrane active peptides are mainly cationic, often primary or secondary amphipathic, and they interact with membranes depending on the composition of the bilayer lipids. Sequences of these peptides consist of short 5-30 amino acid sections derived from natural proteins or synthetic sources. Membrane active peptides can be designed using computational methods or can be identified in screenings of combinatorial libraries. This review focuses on strategies that were successfully applied to the design and optimization of membrane active peptides with respect to the fact that diverse features of successful peptide candidates are prerequisites for biomedical application. Not only membrane activity but also degradation stability in biological environments, propensity to induce resistances, and advantageous toxicological properties are crucial parameters that have to be considered in attempts to design useful membrane active peptides. Reliable assay systems to access the different biological characteristics of numerous membrane active peptides are essential tools for multi-objective peptide optimization.

In this research the engine performance of biodiesel made with castor oil through homogeneous alkali catalyzed transesterification was analyzed. The input variables for the performance analysis were biodiesel blend and engine speed while the response variables were break power (BP), basic specific fuel consumption (BSFC), break thermal efficiency (BTE), torque and unit cost. The engine performance was modeled using artificial neural network (ANN) and the ANN was subsequently used as the objective function for a non dominated sorting genetic algorithm (NSGA-II) for multi objective optimization of the engine performance. The ANN was equally coupled with a desirability function whose outputs were optimized using simulated annealing for multi objective optimization of the engine performance. Subsequent comparison of the two optimization models was done. The results show that biodiesel from castor oil could be a good replacement for biodiesels from fossil fuels. The ANN model predicted engine performance very well with the lowest value of the correlation coefficient between the experimental responses and ANN predictions being 0.9733. The multi objective optimization using desirability function performed excellently well with the optimum blend and speed being 78.7% and 1754.48 rpm respectively. The Pareto front from the NSGA-II algorithm generally has high desirability values. The Pareto front solution which is more flexible than the desirability function solution would serve as an excellent guide for engine designers. Finally, castor oil based biodiesel cost was for the first time integrated into engine performance optimization studies.

Oil pipeline network system health monitoring is important primarily due to the high cost of failure consequences. Optimal sensor selection helps provide more effective system health information from the perspective of economic and technical constraints. Optimization models confront different issues. For instance, many oil pipeline system performance models are inherently nonlinear, requiring nonlinear modelling. Optimization also confronts modeling uncertainties. Oil pipeline systems are among the most complicated and uncertain dynamic systems, as they include human elements, complex failure mechanisms, control systems, and most importantly component interactions. In this paper, an entropy-based Bayesian network optimization methodology for sensor selection and placement under uncertainty is developed. Entropy is a commonly used measure of information often been used to characterize uncertainty, particularly to quantify the effectiveness of measured signals of sensors in system health monitoring contexts. The entropy based Bayesian network optimization outlined herein also incorporates the effect that sensor reliability has on system information entropy content, which can also be related to the sensor cost. This approach is developed further by incorporating system information entropy and sensor costs in order to evaluate the performance of sensor combinations. The paper illustrates the approach using a simple oil pipeline network example. The so-called particle swarm optimization algorithm is used to solve the multi-objective optimization model, establishing the Pareto frontier.

The research of innovative methodologies to improve the Aluminium alloys formability at room temperature still remains an open question: the local modification of the material properties via short-term heat treatments followed by the stamping at room temperature is reported to be an effective alternative to the forming in warm conditions. In the present work, such a methodology has been applied to the deep drawing of an age-hardenable Aluminium alloy (AA6082-T6) using an experimental/numerical approach. A preliminary extensive material characterization was aimed at investigating the material behaviour: (i) in the as-received condition (peak hardening), (ii) in the supersaturated condition (obtained by physical simulation) and (iii) after being locally solutioned via laser heating. A Finite Element based approach (Abaqus CAE, v. 6.17) was then used to design the laser treatment of the blanks to be subsequently deep drawn at room temperature: a 2D axisymmetric model of the deep drawing process was coupled with the optimization platform modeFRONTIER in order to define the radial extent of the laser heat treated area able to maximize the Limit Drawing Ratio. The experimental tests were finally conducted for validation purposes and revealed the effectiveness of the adopted approach which allowed to improve the drawability of more than 20% with respect to the as received condition (T6).

OBJECTIVE: In cancer therapy optimization, an optimal amount of drug is determined to not only reduce the tumor size but also to maintain the level of chemo toxicity in the patient\'s body. The increase in the number of objectives and constraints further burdens the optimization problem. The objective of the present work is to solve a Constrained Multi- Objective Optimization Problem (CMOOP) of the Cancer-Chemotherapy. This optimization results in optimal drug schedule through the minimization of the tumor size and the drug concentration by ensuring the patient\'s health level during dosing within an acceptable level.
METHODS: This paper presents two hybrid methodologies that combines optimal control theory with multi-objective swarm and evolutionary algorithms and compares the performance of these methodologies with multi-objective swarm intelligence algorithms such as MOEAD, MODE, MOPSO and M-MOPSO. The hybrid and conventional methodologies are compared by addressing CMOOP.
RESULTS: The minimized tumor and drug concentration results obtained by the hybrid methodologies demonstrate that they are not only superior to pure swarm intelligence or evolutionary algorithm methodologies but also consumes far less computational time. Further, Second Order Sufficient Condition (SSC) is also used to verify and validate the optimality condition of the constrained multi-objective problem.
CONCLUSIONS: The proposed methodologies reduce chemo-medicine administration while maintaining effective tumor killing. This will be helpful for oncologist to discover and find the optimum dose schedule of the chemotherapy that reduces the tumor cells while maintaining the patients\' health at a safe level.

BACKGROUND: Targeted amplicon sequencing of the 16S ribosomal RNA gene is one of the key tools for studying microbial diversity. The accuracy of this approach strongly depends on the choice of primer pairs and, in particular, on the balance between efficiency, specificity and sensitivity in the amplification of the different bacterial 16S sequences contained in a sample. There is thus the need for computational methods to design optimal bacterial 16S primers able to take into account the knowledge provided by the new sequencing technologies.
RESULTS: We propose here a computational method for optimizing the choice of primer sets, based on multi-objective optimization, which simultaneously: 1) maximizes efficiency and specificity of target amplification; 2) maximizes the number of different bacterial 16S sequences matched by at least one primer; 3) minimizes the differences in the number of primers matching each bacterial 16S sequence. Our algorithm can be applied to any desired amplicon length without affecting computational performance. The source code of the developed algorithm is released as the mopo16S software tool (Multi-Objective Primer Optimization for 16S experiments) under the GNU General Public License and is available at http://sysbiobig.dei.unipd.it/?q=Software#mopo16S .
CONCLUSIONS: Results show that our strategy is able to find better primer pairs than the ones available in the literature according to all three optimization criteria. We also experimentally validated three of the primer pairs identified by our method on multiple bacterial species, belonging to different genera and phyla. Results confirm the predicted efficiency and the ability to maximize the number of different bacterial 16S sequences matched by primers.