Evolutionary Large-Scale Multi-Objective Optimization: A Survey

Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.

Analysis of Consumer Behavior on Buying Decision at the Online Shop, Easy Shopping Indonesia

Know the effect of the consumer behavior of online shop, Easy Shopping Indonesia on the consumers’ buying decision. The quantitative approach, descriptive and verified design are used. As much as 100 consumers of Essay Shopping in Bandung are selected as respondents using a purposive method. The instrument of data collection is questionnaire. The data analysis used is Structural Equation Modeling based on Partial Least Square (PLS SEM). The research results show that the consumer behavior of Easy Shopping online shop affects the consumer buying decision significantly. The biggest effect of the consumer behavior dimension on the buying decision variable is a personal factor; the second one is a psychological factor; the third one is a social factor and the fourth one is a cultural factor. The biggest effect of the consumer behavior variable is on the dimension of payment method; then the second one is distribution selection; the third one is on brand selection; the fourth one is on buying time and the last one is on product selection. Those findings are the antecedents of the online buying decision which finally affect the changes of the buying decision from the conventional buying process into the online one. Key Words : consumer behavior, cultural factors, social factors, personal factors and psychological factors, buying decision, product selection, brand selection, distribution selection, payment method and timing.

Bike-Sharing Fleet Allocation Optimization Based on Demand Gap and Cycle Rebalancing Strategies

In Bike-Sharing System (BSS), the initial number of bikes at station will affect the time interval and the amount of rebalancing, which is usually empirically determined and does not reflect the characteristics of consumer demand in finer time granularity, thus possibly leading to biased conclusions. In this paper, a fleet allocation method considering demand gap is first proposed to calculate the initial number of bikes at each station. Then, taking the number of demand gap periods as the decision variable, an optimization model is built to minimize the total rebalancing amount. Furthermore, the research periods are divided into multiple subcycles, the single-cycle and multicycle rebalancing strategies are presented, and the additional subcycle rebalancing method is introduced to amend the number of bikes between subcycles to decrease the rebalancing amount of the next subcycle. Finally, our methods are verified in effectively decreasing the rebalancing amount in a long-term rebalancing problem.

Query-focused multi-document text summarization using fuzzy inference

The present paper proposes a fuzzy inference system for query-focused multi-document text summarization (MTS). The overall scheme is based on Mamdani Inferencing scheme which helps in designing Fuzzy Rule base for inferencing about the decision variable from a set of antecedent variables. The antecedent variables chosen for the task are from linguistic and positional heuristics, and similarity of the documents with the user-defined query. The decision variable is the rank of the sentences as decided by the rules. The final summary is generated by solving an Integer Linear Programming problem. For abstraction coreference resolution is applied on the input sentences in the pre-processing step. Although designed on the basis of a small set of antecedent variables the results are very promising.

THE EFFECT OF CONSUMERS’ BEHAVIOR AND PERCEIVED RISK ON PURCHASE DECISIONS AMONG ONLINE SHOP CONSUMERS

The goal of the research is to know the effect of the consumer behavior and perceived risk on, the consumers’ buying decision. This research employs a quantitative approach, descriptive and verified design. As much as 100 consumers are selected as respondents using a purposive method. The instrument of data collection is questionnaire. The data analysis used is Structural Equation Modeling. The research results show that the consumer behavior and perceived risk affects the consumer buying decision significantly. The biggest effect of the consumer behavior dimension on the buying decision variable is a personal factor and the biggest effect of the consumer behavior variable is on payment method dimension. The perceived risk contributes to the consumer buying decision significantly. It also proves that that the emerging of the online shops contributes shifting of the consumer behavior from a conventional business transaction into a digital one among the consumers.

Opposing regulation of short-term memory by basal ganglia direct and indirect pathways that are coactive during behavior

The basal ganglia direct and indirect pathways are viewed to mediate opposing functions in movement. However, this classic model is challenged by recent findings that both pathways are coactive during behavior. We examined the roles of direct (dSPNs) and indirect (iSPNs) pathway spiny projection neurons in a decision-making task with a short-term memory (STM) component. Optogenetic stimulation of cortical-input-defined dSPNs and iSPNs during STM oppositely biased upcoming licking choice, without affecting licking execution. Optogenetically identified dSPNs and iSPNs showed similar response patterns, although with quantitative difference in spatiotemporal organization. To understand how coactive dSPNs and iSPNs play opposing roles, we recorded population activity in frontal cortex and the basal ganglia output nucleus SNr. Stimulation of dSPNs and iSPNs bidirectionally regulated cortical decision variable through the differential modulation of SNr ramping activity. These results reconcile different views by demonstrating that coactive dSPNs and iSPNs precisely shape cortical activity in a push-pull balance.

Triple crossing positivity bounds for multi-field theories

We develop a formalism to extract triple crossing symmetric positivity bounds for effective field theories with multiple degrees of freedom, by making use of su symmetric dispersion relations supplemented with positivity of the partial waves, st null constraints and the generalized optical theorem. This generalizes the convex cone approach to constrain the s2 coefficient space to higher orders. Optimal positive bounds can be extracted by semi-definite programs with a continuous decision variable, compared with linear programs for the case of a single field. As an example, we explicitly compute the positivity constraints on bi-scalar theories, and find all the Wilson coefficients can be constrained in a finite region, including the coefficients with odd powers of s, which are absent in the single scalar case.

The Due Date Assignment Scheduling Problem with Delivery Times and Truncated Sum-of-Processing-Times-Based Learning Effect

This paper considers a single-machine scheduling problem with past-sequence-dependent delivery times and the truncated sum-of-processing-times-based learning effect. The goal is to minimize the total costs that comprise the number of early jobs, the number of tardy jobs and due date. The due date is a decision variable. There will be corresponding penalties for jobs that are not completed on time. Under the common due date, slack due date and different due date, we prove that these problems are polynomial time solvable. Three polynomial time algorithms are proposed to obtain the optimal sequence.

Hidden temporal structure of the ongoing task impacts detection strategy and is reflected in pupillary dynamics

Estimating temporal regularities in incoming sensory inputs supports optimal decisions in noisy environments. In particular, inferred temporal structure can ease the detection of likely target events. Here we postulated that timely urgency signals can adapt subjects' decision-making to the ongoing task temporal structure, possibly through neuromodulatory tone. To test this hypothesis, we used an auditory change detection task in which targets followed a block-based temporal contingency, unbeknownst to participants. False alarm occurrences were driven by the distribution of target timings, indicating that participants adapted their behavior to the ongoing temporal structure. Task-evoked pupillary responses were larger for blocks with earliest target timings, and correlated with individual subjects' behavioral adaptation. Individual pupil responses matched an urgency signal extracted from a decision model fitted to behavior. This work demonstrates that internal temporal expectation can be tracked through pupillary dynamics, suggesting a role of neuromodulatory systems in context-dependent modulation of decision variable dynamics.

Analisis Faktor-Faktor yang Mempengaruhi Minat & Keputusan Mahasiswa Bogor Berinvestasi di Pasar Modal Syariah pada Masa Pandemi(2019-2020)

This study aims to determine what factors influence the interest and decision of Bogor students to invest in the Islamic capital market during the pandemic. The sample in this study were students of the Islamic economics study program class 2017-2018 which consisted of three campuses including Ibnu Khaldun University, Bogor Agricultural University, and the Tazkia Institute with a total of 100 students. The independent variables in this study include benefits, motivation, returns and knowledge, and the dependent variable in this study is the interest variable and the decision variable. The method used in this study is a quantitative method and the data used in this study are primary data obtained directly from the results of distributing questionnaires and measured using a Likert scale. The data analysis technique used in this study is SmartPLS. The data in this study were collected using a questionnaire and then processed using the SmartPLS 3.3.3 program. The results showed that the benefit variable (X1) and the return variable (X3) had a positive and significant effect on the interest variable (Y1) and for the knowledge variable X4 and the interest variable Y1 had a positive and significant influence on the decision variable Y2. Keywords: Benefits, Motivation, Return, Knowledge, Capital Market, Investment