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This article introduces a novel stochastic optimization method termed the Best-Other Algorithm (BOA). The nomenclature reflects its reliance on the best member, which is amalgamated with other entities. BOA, a metaphor-free swarm-based metaheuristic, comprises three directed searches. The first involves subtracting the best member from a randomly selected member. The second entails determining the midpoint between the best member, and another randomly chosen member. The third centers around the midpoint between the best member and a random solution along the space. The efficacy of BOA is evaluated by challenging it to solve a collection of 23 functions. In this evaluation, BOA is pitted against five other metaheuristics: Northern Goshawk Optimization (NGO), Zebra Optimization Algorithm (ZOA), Coati Optimization Algorithm (COA), Migration Algorithm (MA), and Osprey Optimization Algorithm (OOA). The findings indicate the superiority of BOA over its counterparts. BOA outperforms NGO, ZOA, COA, MA, and OOA in 21, 15, 16, 15, and 17 functions, respectively. These results underscore the pivotal role of the best member as a reference and the comparatively lesser significance of the neighborhood search as the search space diminishes during the iteration. [ABSTRACT FROM AUTHOR]

There are two problems in the development of swarm-based metaheuristic. First, there are not any metaheuristic is able to solve all problems superiorly. Second, the cross-dimension mapping between the entity and its reference during performing the directed search is rare to find. Based on these problems, this work introduces a new metaphorfree swarm-based metaheuristic called the triple-shake algorithm (TSA). As its name suggests, TSA consists of three directed searches. The reference in the first search is the balance mixture between the finest entity and the member of permutation set. The reference in the second search is the balance mixture between the finest entity and a randomly chosen entity. The reference in the third search is the finest entity only. But the cross-dimension mapping is performed in this third search with 50 percent probability. In the benchmark assessment, TSA is compared with zebra optimization algorithm (ZOA), walrus optimization algorithm (WaOA), migration algorithm (MA), total interaction algorithm (TIA), and one-toone based optimization (OOBO). The result indicates the dominance of TSA among its comparators. TSA is better than ZOA, WaOA, MA, TIA, and OOBO in 21, 19, 17, 19, and 22 functions consecutively out of 23 functions. [ABSTRACT FROM AUTHOR]

The no-free-lunch (NFL) theory has become the main reason of developing new metaheuristics in decades. Besides, the strict dimension mapping has been implemented in many population-based metaheuristics, especially the swarm-based metaheuristics. Regarding this problem, this paper proposes a new swarm-based metaheuristics called focus and shake algorithm (FSA). FSA has novel approach in the dimension mapping between the agent and its reference during the directed motion. It combines the strict dimension mapping called as focus approach and the randomized dimension mapping called as shake approach to enhance its exploration ability. FSA deploys two directed motions based on two references. The first reference is constructed based on the balance mixture between two finer agents while the second reference is constructed based on the balance mixture of the finest agent and a randomly picked agent. In the competing assessment, FSA competes with five brand new swarm-based metaheuristics: migration algorithm (MA), total interaction algorithm (TIA), lyrebird optimization algorithm (LOA), osprey optimization algorithm (OOA), and kookaburra optimization algorithm (KOA). The result exhibits that FSA is finer than MA, TIA, LOA, OOA, and KOA in 19, 21, 21, 19, and 20 functions out of 23 functions respectively. The result also shows that the superiority of FSA takes place in both unimodal and multimodal problems. In the future, the cross-dimension mapping can be more explored to develop finer swarm-based metaheuristics. [ABSTRACT FROM AUTHOR]

This paper introduces a novel metaheuristic named the stochastic shaking algorithm (SSA), which is rooted in swarm intelligence principles. The innovation lies in its unique utilization of iteration for selecting references during guided searches through a stochastic approach. The optimization process involves two sequential steps: the primary reference in the first step is the finest swarm member, while in the second step, it is the mean of all finer swarm members plus the finest one. This primary reference is then combined with a randomly chosen solution within the space, serving as the secondary reference. SSA undergoes evaluation in two contexts. The first involves assessing its performance using a set of 23 classic functions as a theoretical use case. The second involves tackling the economic load dispatch problem (ELD), a practical use case featuring a system with 13 generators of various energy resources. The study compares SSA against five other metaheuristics--One to One Based Optimization (OOBO), Kookaburra Optimization Algorithm (KOA), Language Education Optimization (LEO), Total Interaction Algorithm (TIA), and Walrus Optimization Algorithm (WaOA). Results indicate SSA's superiority over OOBO, KOA, LEO, TIA, and WaOA in 21, 13, 11, 16, and 14 functions out of 23 functions, respectively. Additionally, the evaluation of the economic load dispatch problem reveals intense competition among the six metaheuristics. [ABSTRACT FROM AUTHOR]

In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions. [ABSTRACT FROM AUTHOR]

Many recent swarm-based metaheuristics are trapped in the exploitation of the highest quality as the main or the only reference and the neighbourhood search with the reduction of local search space during the iteration. Regarding to this issue, this paper introduces a novel metaheuristic called swarm space hopping algorithm (SSHA). SSHA consists of three searches. First, a directed search toward the highest quality is performed. Second, the directed search toward the resultant of better agents or away from the other agent is performed. Third, the arithmetic crossover between the agent and a randomized solution in the first half or second half of space is performed. In this work, three evaluations are performed to assess the performance of SSHA. The first evaluation is the benchmark evaluation to compare the performance of SSHA with other recent metaheuristics: northern goshawk optimization (NGO), zebra optimization algorithm (ZOA), clouded leopard optimization (CLO), osprey optimization algorithm (OOA), and total interaction algorithm (TIA). The result exhibits that SSHA is better than NGO, ZOA, CLO, OOA, and TIA in 21, 20, 17, 17, and 21 functions. In the second evaluation, the individual search evaluation to compare the contribution between the first and second searches is performed, with the result that the second search outperforms the first search. The third evaluation is performed to assess the contribution of the third search in the optimization process, and the result shows that the contribution of the third search is significant only in three functions. [ABSTRACT FROM AUTHOR]

This work presents a new metaheuristic called treble opposite algorithm (TOA). It consists of three phases. There are two searches that are opposite to each other performed in each phase. In the first phase, the search toward and away from the best solution is carried out. In the second phase, the search toward and away from the middle between two randomly picked solutions is carried out. In the third phase, a neighborhood search around the narrow and large space is carried out. A candidate is selected among the two searches in every phase. TOA is challenged to solve theoretical and practical problems. The 23 functions represent theoretical problems, while the portfolio optimization of stocks in the banking sector listed in IDX30 represents the practical problem. TOA is compared with five metaheuristics: grey wolf optimization (GWO), golden search optimization (GSO), average subtraction-based optimization (ASBO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA). The result indicates that TOA is superior to its competitors as it is better than GWO, GSO, ZOA, ASBO, and COA in 22, 23, 19, 20, and 19 functions respectively, in handling 23 functions and produces the highest total capital gain in handling portfolio optimization problem. In the future, TOA can be utilized to handle many other realworld optimization problems. Moreover, TOA can be hybridized with other metaheuristics to improve its performance. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the coati optimization algorithm (COA) and the osprey optimization algorithm (OOA). ECOA is constructed by five searches performed sequentially by the swarm members. The first three are directed searches, while the last two are neighborhood searches. All three directed searches are adopted from COA and OOA. Meanwhile, the four-bordered neighborhood search is developed based on a new approach. During the assessment, ECOA was challenged to overcome the set of 23 functions and contended with five new metaheuristics: total interaction algorithm (TIA), golden search optimization (GSO), average and subtraction-based optimization (ASBO), COA, and OOA. The result shows that ECOA outperforms TIA, GSO, ASBO, COA, and OOA in 16, 23, 18, 21, and 21 functions. Meanwhile, the individual search test result shows that the directed searches perform better than the neighborhood searches. Moreover, the directed search toward the best member becomes the most dominant search. [ABSTRACT FROM AUTHOR]

There has been a massive development of metaheuristic algorithms in the latest decade where swarm intelligence becomes the fundamental approach. Meanwhile, there is still no ideal metaheuristic that can solve all problems superiorly, as declared in the no-free-lunch (NFL) theory. This work introduces a novel swarm-based metaheuristic named as migration-crossover algorithm (MCA). In MCA, the swarm intelligence is enriched with the crossover technique and the neighbourhood search with unbalanced local search space. The global finest solution becomes the reference in the first step while the middle between two stochastically chosen solutions becomes the reference in the second step. The neighbourhood search is performed in the third step. The collection of 23 functions become the use case during the evaluation of MCA. In the first evaluation, MCA is compared with five new metaheuristics: total interaction algorithm (TIA), osprey optimization algorithm (OOA), migration algorithm (MA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA). The result reveals that MCA is finer than TIA, OOA, MA, COA, and WaOA in 20, 19, 17, 20, and 17 functions subsequently. The result of the second evaluation reveals that the global finest solution becomes the dominant contributor in the high dimension functions while the middle between two stochastically chosen solutions becomes the dominant contributor in the fixed dimension functions. [ABSTRACT FROM AUTHOR]

This work offers a new stochastic optimization i.e., a metaheuristic algorithm combining both direction-based search and neighbourhood search called as walk-spread algorithm (WSA). These two types of searches become the inspiration for its name where the term walk represents the direction-based search while the term spread represents the neighbourhood search. There are two direction-based searches performed in every iteration where each search produces a single child. Meanwhile, there are two neighbourhood searches performed in every iteration where each search produces several children. The global best unit becomes the first reference while two shuffled units become the second reference in performing the direction-based search. Meanwhile, the local search space of the first neighbourhood search is wide while the second one is narrow. The 23 classic functions are chosen as the assessment of WSA where WSA is confronted with the five latest metaheuristics: mixed leader-based optimization (MLBO), golden search optimization (GSO), pelican optimization algorithm (POA), zebra optimization algorithm (ZOA), and attack-leave optimization (ALO). The assessment result shows that the offered WSA achieves the acceptable result so fast. Moreover, WSA is also superior to these five confronters by outperforming MLBO, GSO, POA, ZOA, and ALO in 23, 23, 22, 21, and 21 functions respectively. [ABSTRACT FROM AUTHOR]

This study was conducted to promote a new adaptive cone algorithm (ACA) algorithm. ACA is a metaheuristic technique based on swarm intelligence. ACA contains three steps. Each agent moves closer to the global reference in the first step. Then, each agent searches for a better solution around the current solution in the second step. The global reference searches for better solutions around it in the third step. This algorithm is named cone because the local space size declines linearly during the iterative process. ACA introduces a new adaptability model to improve the exploration strategy when a better solution cannot be achieved. It is conducted by enlarging the local solution space. ACA is challenged to find the final solution for theoretical and practical problems. The 23 functions are chosen as theoretical optimization problems. The portfolio optimization problem is selected as the practical problem. ACA is compared with five algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), marine predator optimization (MPA), average subtraction-based optimizer (ASBO), and pelican optimization algorithm (POA). The result shows that ACA is competitive in finding the optimal solution for 23 functions and outperforms all sparing algorithms in achieving the highest total capital gain in tackling the portfolio optimization problem. ACA is superior to PSO, GWO, MPA, ASBO, and POA in solving 20, 11, 13, 4, and 21 functions, respectively. In the future, ACA can be implemented in solving various practical optimization problems. [ABSTRACT FROM AUTHOR]

This work presents a hybrid metaheuristic, namely a hybrid marine predator algorithm - hide object game optimization (MPA-HOGO). MPA-HOGO is built by combining two shortcoming metaheuristics: marine predator algorithm (MPA) and hide object game optimization (HOGO). The distinct strategy in MPA-HOGO is as follows. MPA-HOGO utilizes an exploration-to-exploitation strategy gradually as the iteration goes on. It uses the global best solution evaluated in every iteration to ensure that the final solution comes from the best solution found during the iteration. It also eliminates the normalized score of every solution to reduce the computational process. This metaheuristic is then tested by solving 23 classic functions. MPA-HOGO is benchmarked with particle swarm optimization (PSO), Komodo mlipir algorithm (KMA), MPA, and HOGO. The result indicates that MPA-HOGO is superior among these metaheuristics. MPA-HOGO is also competitive in solving practical optimization problems. Through simulation, MPA-HOGO produces 13%, 7%, and 2% lower total costs than PSO, MPA, and HOGO respectively in solving inventory management problems in the vendor-managed inventory system. [ABSTRACT FROM AUTHOR]

This research introduced a novel simple metaheuristic called as total interaction algorithm (TIA). TIA is a swarm intelligence which relies on the interaction among solutions in the population. The core and distinct concept of TIA is that each solution interacts with all other solutions in every iteration to find the best possible solution. Then, this new alternative replaces the current solution if it is better than the current solution. In this research, TIA is tested to solve 23 functions that represent both unimodal and multimodal problems. TIA is benchmarked with five metaheuristics: particle swarm optimization (PSO), marine predator algorithm (MPA), golden search optimizer (GSO), guided pelican algorithm (GPA), and driving training-based optimizer (DTBO). The result indicates that TIA is superior to other benchmark metaheuristics, especially in solving the high dimension functions. TIA is better than PSO, MPA, GSO, GPA, and DTBO in 22, 21, 16, 11, 13 functions. The result also indicates that the increase of the maximum iteration improves the performance of TIA mostly in solving high dimension unimodal functions. Meanwhile, the increase of the population size is less significant to improve its performance. Overall, this research resumes that the interaction with as many as possible individuals is proven better than with only selected individuals as implemented in many other metaheuristics. [ABSTRACT FROM AUTHOR]

A novel metaheuristic algorithm is proposed in this paper, namely stochastic komodo algorithm (SKA). This proposed algorithm is an improved version of Komodo mlipir algorithm (KMA), which is inspired by the behaviour of Komodo during foraging and mating. The improvement is conducted by simplifying the basic form of KMA. Like KMA, it consists of three types of Komodo: big male, female, and small male. Male Komodo focuses on intensification. On the other side, females conduct diversification or intensification based on the search space radius. It eliminates sorting mechanism at the beginning of the iteration. Rather than determined from the quality (fitness score), the distribution of the types of Komodo is conducted stochastically at the beginning of every iteration. This proposed algorithm is then tested by using ten functions. Five functions are unimodal, while the five others are multimodal. The proposed algorithm is also compared with several well-known algorithms: football game-based optimization, hide objects game optimization, cloud-theory-based simulated annealing, harmony search, and KMA. The result shows that this proposed algorithm is very competitive compared with these benchmark algorithms in both unimodal and multimodal functions. A female-dominant formation is proven to achieve optimal result. [ABSTRACT FROM AUTHOR]

This work proposes a new simple metaheuristic optimization method inspired by the social forces model used in pedestrian dynamics. The proposed model is a swarm-based model where a collective intelligence is shared among the agents, consisting of several persons or agents who walk over the search space to find the best solution. Each time a person finds a better solution, they share it with another person. The proposed model is then evaluated by implementing it to solve ten benchmark functions. The five are multimodal functions (Ackley, Rastrigin, Griewank, Bukin, and Michalewicz) while the other are unimodal (Sphere, Rosenbrock, Bohachevsky, Zakharov, and Booth). The performance is compared with five metaheuristic algorithms: particle swarm optimization, darts game optimizer, shell game optimization, marine predator algorithm, and Komodo mlipir algorithm. The simulation result shows that the proposed method is competitive enough to solve multimodal and unimodal functions. The performance is superior in solving Michalewicz and Zakharov functions but less competitive in solving the Bukin function. The results also imply that there is no single algorithm that is the best in solving all kinds of problems. [ABSTRACT FROM AUTHOR]

Multi-depot capacitated vehicle routing problem is a derivative of vehicle routing problem and a wellknown area that has been studied widely with various applications and constraints. Many studies in it focused on minimizing cost and distance with assumption that all nodes are visited. Ironically, number of unserved customers (nodes) in a single trip was rarely explored and often ignored. Based on this problem, this work aims to minimize the total travel distance and the number of unserved customers in single routing cycle. The solution is developed by combining the stable marriage algorithm and k-means clustering in the clustering process. The nearest neighbour algorithm is used in the routing process. This work proposes two contributions. The first is the usage of the stable marriage and k-means clustering. The second is concerning the predetermined distribution of the vehicles in the solution to reduce the number of unserved customers. In the simulation, this proposed model is compared with the hybrid evolutionary algorithm (HEA), partition-based algorithm-nearest neighbour algorithm (PBA-NN), genetic algorithm-nearest neighbour algorithm (GA-NN) and simulated annealing algorithm (SA). Based on the simulation result, it is found that the proposed model performs moderately as a trade-off between the SA and PBA-NN. In the number of unserved customers aspect, when the number of customers is low, the proposed model creates 71 percent lower than the PBA-NN model. Meanwhile, when the number of customers is high, the proposed model creates 73 percent lower than the PBA-NN model. In the total travel distance aspect, when the number of customers is low (50 persons), the proposed model creates 63 percent higher than the GA-NN model and 48 percent lower than the SA model. Meanwhile, when the number of customers is high (100 customers), the proposed model creates 52 percent higher than the PBA-NN model and 54 percent lower than the SA model. [ABSTRACT FROM AUTHOR]