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Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.

The purpose of this paper is to propose a multi-objective differential evolution algorithm named as MOMR-DE to resolve multicast routing problem. In mobile ad hoc network (MANET), multicast routing is a non-deterministic polynomial -complete problem that deals with the various objectives and constraints. Quality of service (QoS) in the multicast routing problem mainly depends on cost, delay, jitter and bandwidth. So the cost, delay, jitter and bandwidth are always considered as multi-objective for designing multicast routing protocols. However, mobile node battery energy is finite and the network lifetime depends on node battery energy. If the battery power consumption is high in any one of the nodes, the chances of network’s life reduction due to path breaks are also more. On the other hand, node’s battery energy had to be consumed to guarantee high-level QoS in multicast routing to transmit correct data anywhere and at any time. Hence, the network lifetime should be considered as one objective of the multi-objective in the multicast routing problem.

Recently, many metaheuristic algorithms formulate the multicast routing problem as a single-objective problem, although it obviously is a multi-objective optimization problem. In the MOMR-DE, the network lifetime, cost, delay, jitter and bandwidth are considered as five objectives. Furthermore, three QoS constraints which are maximum allowed delay, maximum allowed jitter and minimum requested bandwidth are included. In addition, we modify the crossover and mutation operators to build the shortest-path multicast tree to maximize network lifetime and bandwidth, minimize cost, delay and jitter.

Two sets of experiments are conducted and compared with other algorithms for these problems. The simulation results show that our proposed method is capable of achieving faster convergence and is more preferable for multicast routing in MANET.

In MANET, most metaheuristic algorithms formulate the multicast routing problem as a single-objective problem. However, this paper proposes a multi-objective differential evolution algorithm to resolve multicast routing problem, and the proposed algorithm is capable of achieving faster convergence and more preferable for multicast routing.

To improve the accuracy of stock price trend prediction in the field of quantitative financial trading, this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory (LSTM).

In this paper, an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.

The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models. Furthermore, the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.

(1) The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination. Compared with other models, it has higher accuracy in predicting the up and down trend of stock prices in the next day. (2) Adopting real coding, elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm, the algorithm is computationally efficient and the results are more likely to converge to the global optimum.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.

In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.

In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

The deployment of blockchain technology (BT) throughout the supply chain is usually led by large firms that dominate the supply chain. Leading firms can encourage other resource-constrained partners to get on board by providing technical and financial support. However, due to the uncertain consequences of relying on leading firms, these partners may still be reluctant to adopt BT. Drawing on resource dependence theory, this study aims to investigate whether and when supply chain alignment can be used as a dependency coping strategy to increase the willingness of resource-constrained partners to adopt BT. Moreover, it aims to examine the motivators for supply chain alignment.

This study adopted a survey research design and collected data from 364 small and medium-sized enterprises in China.

Supply chain alignment positively affects BT adoption. The effect of supply chain alignment on BT adoption is contingent on guanxi (a Chinese cultural tradition of interpersonal connections that facilitate a mutual exchange of favors). Relative advantage, technology complexity, organizational readiness and cost are motivators for supply chain alignment. Supply chain alignment mediates the effect of cost, technology complexity and relative advantage on BT adoption.

This research addresses the problem of resource dependency in the context of BT adoption which has been overlooked by previous research. Moreover, this paper enriches the BT literature by identifying supply chain alignment as an important channel for technology–organization–environment factors to influence BT adoption.

This paper aims to explore how intra-industry entrepreneurial experience and failure entrepreneurial experience affect novelty-centered business model design in a new venture. Moreover, the authors also consider whether the contingent value of entrepreneurial experience may differ according to competitive intensity.

A survey via questionnaire was conducted with 290 entrepreneurs and top managers from Chinese new ventures that provided the research data. Hierarchical regression analysis was used to test the proposed theoretical hypotheses.

The empirical results indicate that intra-industry entrepreneurial experience has an inverted U-shaped effect on novelty-centered business model design, whereas failure entrepreneurial experience has a negative effect on novelty-centered business model design. Furthermore, the authors also find that competitive intensity weakens the inverted U-shaped effect of intra-industry entrepreneurial experience on novelty-centered business model design.

This study offers new insights into the effects of intra-industry entrepreneurial experience and failure entrepreneurial experience on novelty-centered business model design and provides useful suggestions for new ventures to promote business model design.

The purpose of this paper is to examine the moderating role of marital status between infidelity and development of stress, anxiety and depression. Additionally, to investigate the relationship among infidelity, stress, anxiety and depression among married couples and divorced individual.

A purposive sampling technique was used based on cross-sectional design. In total, 200 participants (married couples, n=100; divorced individuals, n=100) were incorporated from different NGO’s and welfare organizations of Rawalpindi, and Islamabad, Pakistan. Age ranged from 20 to 60 years. Two scales were used to measure the infidelity, stress, anxiety and depression in married couples and divorced couples.

The result revealed that emotional infidelity was positively significant correlated with stress (r=0.39, p=0.001), anxiety (r=0.40, p=0.001) and depression (r=0.35, p=0.001) for married couples. The result also displayed that sexual infidelity was positively significant correlated with stress (r=0.39, p=0.001), anxiety (r=0.39, p=0.001) and depression (r=0.34, p=0.001) for married couples. The result further elaborated that emotional infidelity and sexual infidelities were positively non-significant correlated with stress, anxiety and depression for divorced individuals. The analysis results revealed that marital status was moderator between infidelity and development of stress, anxiety and depression.

This paper consisted of sample from three basic cities of Pakistan; thus, this paper finding may not be applied on whole population. Consequently, explanatory, exploratory and descriptive studies would be useful to enlighten the infidelity’s mechanism in prolongation of psychological distress across married couples and divorced individual in detail. Local tools to measure gender-related issues would be helpful in prospect while it combine cultural aspects as well.

This study would be helpful in clinical settings to raise the awareness to effectively deal with their children.

The study recommended that those divorced individuals who had experienced either sexual infidelity or emotional infidelity were more likely to develop psychological problems as compared to married couples. This study would be helpful in clinical settings to raise the awareness to effectively deal with their children.

This paper examines the impact of the COVID-19 pandemic on low-carbon consumption of dairy products through informational interventions. The empirical findings seek to enlighten developing countries' efforts in coping with climate change and potential dietary transitions.

A randomized controlled trial was designed to examine the effects of purpose-differentiated information interventions on individual dairy consumption. The experiment recruited and randomly assigned 1,002 college students into four groups to receive (or not) environmental or/and health information interventions.

The empirical analysis finds that health and combined information interventions have a positive impact on dairy consumption, while environmental information interventions' effect on dairy consumption is insignificant. In the context of the pandemic, health information interventions positively affected participants' perceptions and preferences for dairy products by delivering knowledge about their role in boosting immunity. However, environmental information interventions failed to do the same things as their insignificant effects on both perception and preference.

Macro-external shocks, such as public health events, may offset the impact of universal information interventions promoting pro-environmental behaviors. For a smooth dietary transition to achieve long-term environmental sustainability, diverse stakeholders must be included in more individualized interventions to guide daily consumption, especially in developing countries with large populations.